Macroeconomic Policy Coordination and Economic Resilience in Indonesia: A Time-Varying Analysis under Global Uncertainty

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We test this concept empirically for Indonesia using a Time-Varying Parameter Vector Autoregression (TVP-VAR) model with stochastic volatility estimated on quarterly data spanning 2000Q1–2023Q4. A seven-variable system incorporates real GDP growth, CPI inflation, the Bank Indonesia policy rate, real government expenditure, the IDR/USD exchange rate, foreign reserves, and the World Uncertainty Index (WUI). Policy coordination is operationalized through a Sign-Concordance Coordination Index (SCCI) grounded in the sign-restricted VAR literature and game-theoretic models of fiscal-monetary interaction. Time-varying impulse response functions and forecast error variance decompositions are computed across three crisis regimes: the 2008 Global Financial Crisis, the 2020 COVID-19 shock, and the 2022–2023 global tightening cycle. Results show that fiscal-monetary complementarity as captured by the SCCI significantly amplifies output resilience, with each unit increase in coordination associated with approximately 0.4 percentage points of additional GDP recovery per quarter following an uncertainty shock. The relationship is non-linear: coordination effects are strongest when foreign reserve buffers exceed approximately 10% of GDP, suggesting a threshold-dependent mechanism. Extensive robustness checks confirm findings across alternative orderings, lag specifications, uncertainty measures, and subsamples. These results extend Martin's (2012) evolutionary resilience framework to the macroeconomic policy domain and provide new evidence that policy interactions, rather than individual instruments, are an important determinant of emerging market resilience. JEL Classification: E52, E62, E63, F41, O53, C32 Econometrics Macroeconomics Coordination-Conditioned Resilience fiscal-monetary coordination TVP-VAR World Uncertainty Index Indonesia emerging markets Sign-Concordance Index Figures Figure 1 1. Introduction Emergiing market economies face a structural tension at the heart of macroeconomic management: they must simultaneously pursue domestic stabilization objectives while remaining exposed to global shocks they cannot control. Indonesia the largest economy in Southeast Asia and the fourth most populous nation on Earth provides a compelling laboratory for studying this tension. Over the past two and a half decades, Indonesia has weathered a succession of major external crises: the residual aftershocks of the 1997 Asian Financial Crisis, the 2008 Global Financial Crisis (GFC), the COVID-19 pandemic recession of 2020, and the global monetary tightening cycle of 2022–2023. In each episode, the interplay between Bank Indonesia's monetary decisions and the government's fiscal stance has determined both the depth of the shock and the speed of recovery. The conventional macroeconomic literature has long recognized that the interaction between monetary and fiscal policy matters. However, most empirical studies examine these policies in isolation assessing the independent effects of interest rate changes on output, or the GDP multiplier of government spending. without modelling how the two instruments interact and co-evolve. This limitation is especially consequential for emerging markets, where the Mundell-Fleming trilemma means that fiscal and monetary policies are fundamentally intertwined as adjustment mechanisms. A second limitation is reliance on linear, time-invariant models that assume structural stability across profoundly different shock episodes. This paper addresses both limitations through a unified empirical and conceptual framework. We make four distinct contributions. First, and most importantly, we introduce the concept of Coordination-Conditioned Resilience (CCR): the proposition that an emerging market economy's resilience to external uncertainty is not a fixed structural property, but a dynamic, policy-contingent outcome that depends on the degree of fiscal-monetary complementarity, itself conditioned by institutional credibility and reserve adequacy. This concept bridges the macroeconomic policy coordination literature and the economic resilience literature — two bodies of work that have evolved largely in parallel without explicit theoretical connection. Second, we operationalize the CCR concept empirically through a Sign-Concordance Coordination Index (SCCI), grounded in the sign-restriction VAR literature (Büyükbaşaran et al., 2020 ) and game-theoretic models of fiscal-monetary interaction (Salimi et al., 2025 ; Chibi et al., 2024 ; Stawska et al., 2019 ), and we demonstrate that this index has significant predictive power for output recovery trajectories. Third, we employ a TVP-VAR model with stochastic volatility the state-of-the-art framework for regime-dependent macroeconomic dynamics to generate time-varying IRFs and FEVDs that trace how policy coordination effectiveness evolves across crisis episodes. Fourth, we document a non-linear, threshold-dependent relationship between reserve adequacy and coordination effectiveness: the output-stabilizing effects of fiscal-monetary complementarity are substantially larger when foreign reserves exceed approximately 10% of GDP, consistent with Aizenman's (2019) quadrilemma framework. These contributions together yield new, actionable evidence on how policy interactions shape macroeconomic resilience in a major emerging market. Unlike prior studies that rely on static or single-equation models, and unlike TVP-VAR studies of Indonesia that focus on external spillovers (Danladi et al., 2024), to the best of our knowledge, this paper contributes by: (i) introduce CCR as a formally defined theoretical concept linking policy coordination to dynamic resilience; (ii) construct and validate a theoretically grounded Sign-Concordance Coordination Index for Indonesia; (iii) document the threshold-conditioned non-linearity between reserve buffers and coordination effectiveness; and (iv) provide comprehensive robustness evidence across five alternative specifications. These contributions are not merely an application of existing tools to a new country they generate testable theoretical propositions that may be applicable to a broader set of open emerging market economies operating under the Mundell-Fleming trilemma. The remainder of the paper is structured as follows. Section 2 reviews the relevant literature across four strands. Section 3 presents the theoretical framework, culminating in the CCR concept and three testable hypotheses. Section 4 describes data, the SCCI construction, and the TVP-VAR methodology. Section 5 presents the main empirical results. Section 6 provides comprehensive robustness analysis. Section 7 discusses findings and policy implications. Section 8 concludes. 2. Literature Review 2.1 Policy Coordination: Theory and Evidence The theoretical case for fiscal-monetary coordination rests on the insight that each instrument's effectiveness depends on the other's posture. In canonical New Keynesian models, optimal stabilization requires monetary policy to anchor expectations while fiscal policy smooths the cycle. When these roles conflict because of fiscal dominance, zero-lower-bound constraints, or trilemma pressures neither instrument achieves its potential. The formal literature on fiscal-monetary games, beginning with Sargent and Wallace (1981) and Bassetto and Hall ( 2020 ), demonstrates that the equilibrium outcome depends critically on which authority moves first and whether the game is cooperative or non-cooperative. Stawska et al. ( 2019 ) show formally that the Nash equilibrium of the non-cooperative fiscal-monetary game yields suboptimal outcomes relative to the cooperative Pareto solution, providing a rigorous justification for studying coordination as a distinct policy objective. Chibi et al. ( 2024 ) extend this framework to Algeria and show that the cooperative Pareto equilibrium yields the lowest welfare loss in response to both demand and supply shocks. Salimi et al. ( 2025 ) apply Nash equilibrium analysis to Hungary and document systematic deviations between actual policies and computed equilibrium strategies, with misalignments contributing to higher public debt and inflation. For emerging markets, the empirical literature on fiscal-monetary interaction has grown substantially. Büyükbaşaran et al. ( 2020 ) employ a Bayesian SVAR with sign and zero restrictions for Turkey and find that the two policies are complements in response to demand and supply shocks but substitutes in response to own-policy shocks a distinction that motivates our use of sign-based coordination measures. Luan et al. ( 2021 ) use a TVP-VARMA for China and identify the sign of the cross-policy response as the key indicator of complement versus substitute behavior: when the response of government spending and money supply to each other's shock carry the same sign, the policies act as complements. This insight directly informs our SCCI construction. For Indonesia specifically, Retnowati et al. ( 2024 ) analyze fiscal and monetary effects on growth using multiple linear regression, while Sriyanto et al. ( 2021 ) employ ARDL cointegration to study government stimulus effects. Saputra et al. (2021) document the structured policy response to COVID-19 through Indonesia's PEN program, and Rizal et al. (2025) analyze Bank Indonesia's role in maintaining financial stability during 2021–2023. These studies provide valuable context but cannot capture the time-varying, interactive nature of policy coordination that is central to our analysis. 2.2 TVP-VAR Methodology The TVP-VAR model with stochastic volatility, established by Primiceri ( 2005 ) and further developed by Nakajima ( 2011 ), allows both coefficients and error variances to vary at each point in time. This framework has become the standard approach for analyzing macroeconomic relationships across crisis regimes. Antonakakis et al. ( 2019 ) demonstrate its power in capturing heterogeneous international monetary policy spillovers. Rodríguez et al. (2023) apply TVP-VAR-SV models to Peru a comparable small open economy and document that the impacts of external shocks differ substantially under high inflation, crisis, and policy change regimes, with greater impacts during high-uncertainty episodes. For Indonesia, Danladi et al. (2024) use a TVP-VAR-SV to analyze U.S. monetary policy spillovers and find substantial time variation in domestic responses corresponding to the GFC, the 2013–2014 taper tantrum, and COVID-19. Our paper builds directly on this foundation but makes two critical departures. First, we focus on domestic policy coordination rather than external spillovers. Second, by combining the TVP-VAR with a formally grounded coordination index, we can ask not just 'how did the economy respond?' but 'how did the coordination of policies shape that response?' a question that single-equation and standard VAR approaches cannot address. 2.3 Economic Resilience Theory Martin ( 2012 ) and Simmie and Martin ( 2010 ) propose an evolutionary conceptualization of economic resilience comprising resistance (the depth of shock reaction), recovery (the speed and magnitude of rebound), and reorientation (the capacity to adapt to a new growth trajectory). Martin et al. ( 2016 ) apply this framework empirically to UK regional employment across four recessions. Trippl et al. (2023) extend the framework to 'transformative resilience,' arguing that crises may represent windows for structural transition. At the national macroeconomic level, Loayza et al. (2020) argue that resilience in developing countries depends critically on fiscal space, monetary transmission effectiveness, and their coordination. Maran ( 2023 ) demonstrates for Indonesia that macroprudential policy tightening improves growth-at-risk, operationalizing the left tail of the GDP distribution as a resilience measure. Our paper builds on and complements Martin’s framework to the macroeconomic policy domain by demonstrating that resilience is not merely a function of economic structure but of the policy coordination regime a point that has not been made in the existing literature. 2.4 Global Uncertainty and Indonesia Ahir et al. (2018) construct the World Uncertainty Index (WUI) and document that uncertainty innovations foreshadow significant output declines in a panel VAR setting. Chowdhury et al. ( 2021 ) confirm negative uncertainty effects on global markets across quantiles. Glebocki and Huber (2024) document that global uncertainty spikes produce immediate exchange rate depreciation and exchange market pressure in emerging markets. For Indonesia, Danladi et al. (2024) and Ibrahim et al. (2023) confirm that Bank Indonesia's credibility is a key mediating factor in how external uncertainty transmits to domestic outcomes, providing direct motivation for including credibility-related variables (reserves, exchange rate) in our coordination framework. 3. Theoretical Framework and the CCR Concept 3.1 Open-Economy Policy Constraints The Mundell-Fleming model characterizes the interaction of monetary and fiscal policies under varying exchange rate regimes and degrees of capital mobility. For Indonesia — a managed float economy with partial capital mobility both instruments operate with partial effectiveness, and their interaction determines the macroeconomic outcome. Aizenman ( 2019 ) formalizes the modern 'quadrilemma': beyond the standard trilemma, financial stability serves as a fourth policy goal, and precautionary reserve management has become a key mechanism through which emerging markets navigate the quadrilemma. This directly implies that the effectiveness of fiscal-monetary coordination is conditioned by the reserve buffer, motivating our threshold analysis. The game-theoretic literature on fiscal-monetary interaction provides the formal grounding for our coordination concept. In the non-cooperative Nash equilibrium, each authority optimizes independently and the outcome is typically Pareto suboptimal (Stawska et al., 2019 ; Kruš., 2017). In a cooperative equilibrium, authorities internalize each other's reaction functions and achieve lower aggregate welfare loss (Chibi et al., 2024 ; Serkov et al., 2024 ). The deviation of actual policy behavior from the Nash equilibrium provides an empirically measurable indicator of coordination. In our TVP-VAR framework, we operationalize this deviation through the sign of cross-policy responses: when both instruments respond to a common shock in the same stabilizing direction (complementarity), actual behavior approximates the cooperative equilibrium; when they diverge (substitutability), behavior approximates the non-cooperative Nash solution. 3.2 The Sign-Concordance Coordination Index (SCCI) Building on the insight of Luan et al. ( 2021 ) that the sign of cross-policy responses indicates complement versus substitute behavior, and on Büyükbaşaran et al. ( 2020 ) who use sign and zero restrictions to formally categorize fiscal-monetary interaction, we define the Sign-Concordance Coordination Index (SCCI) as follows. At each point in time t, we extract the time-varying impulse response of the policy rate (IRᵖᵈᵒᵌₜ) and government expenditure (IRᵓᵇᵂₜ) to a one-standard-deviation positive WUI shock at horizon h = 4 quarters. The SCCI at time t is defined as: SCCIₜ = −sign(IRᵖᵈᵒᵌₜ) × sign(IRᵓᵇᵂₜ) The negative sign on the policy rate response reflects the convention that a negative interest rate response (monetary easing) to an uncertainty shock is countercyclical. The SCCI = + 1 when both instruments respond countercyclically (monetary easing AND fiscal expansion: complementarity); SCCI = − 1 when they diverge (one eases while the other tightens: substitutability); and intermediate values are possible for weighted extensions. This formulation is theoretically defensible because it is directly grounded in the sign-restriction literature, transparent and reproducible, not subject to the 'ad hoc' critique levelled at correlation-based indices, and it exactly maps to the cooperative versus non-cooperative equilibrium distinction in the game-theoretic literature. An important feature of the SCCI is that it is shock-conditional: it measures coordination specifically in response to global uncertainty shocks, rather than capturing the unconditional comovement of policy instruments. This is the appropriate measure for our purpose, since we are interested in how coordinated the policy response is precisely when the economy is under external stress. 3.3 Coordination-Conditioned Resilience (CCR): The Core Concept We now introduce the paper's central theoretical contribution. Economic resilience in the standard Martin ( 2012 ) framework is treated as a property of economic structure — the composition of industries, labour market flexibility, and so on. We propose that for open emerging market economies, macroeconomic resilience is additionally, and perhaps primarily, a function of the policy coordination regime. Definition (Coordination-Conditioned Resilience). An economy exhibits Coordination-Conditioned Resilience to a global uncertainty shock if, and to the degree that, fiscal and monetary authorities respond in a complementary (cooperative-equilibrium-approximating) manner, with the strength of this resilience effect conditioned by the availability of external buffer capacity (measured by reserve adequacy) that relaxes the exchange rate constraints otherwise binding on monetary easing. This definition generates three testable hypotheses: H1 : The effectiveness of policy coordination (SCCI) in buffering global uncertainty shocks on GDP varies significantly across crisis regimes. Periods of higher SCCI are associated with smaller output losses and faster recoveries. H2 : The output-stabilizing effect of coordination is non-linear and threshold-dependent: the marginal effect of SCCI on GDP recovery is significantly larger when foreign reserves exceed a threshold level (approximately 10% of GDP), consistent with the quadrilemma framework. H3 : Fiscal and monetary policy act as complements (SCCI = + 1) in response to large demand-shock-type episodes (GFC, COVID-19), but exhibit greater substitutability (SCCI → −1) during supply-shock or external monetary pressure episodes (2013–2015 taper tantrum, 2022–2023 global tightening). The CCR concept extends Martin's (2012) evolutionary resilience framework by adding a policy coordination dimension absent from the original regional economics literature. It also extends the policy coordination literature which focuses primarily on welfare loss functions and strategic equilibria — by linking coordination outcomes to the dynamic GDP trajectory rather than to static welfare comparisons. 4. Data and Methodology 4.1 Data Description and Sources Our analysis uses quarterly data for Indonesia spanning 2000Q1 to 2023Q4 (96 observations), sourced from publicly available macroeconomic databases. The sample begins with Indonesia's post-crisis stabilization period following central bank independence (1999) and the start of the modern macroeconomic policy regime. Table 1 summarizes the seven variables. Table 1 Variable Description, Sources, and Transformations Variable Role Source Transformation Series Code Real GDP Growth Economic resilience proxy IMF via FRED QoQ growth rate (%) NGDPRSAXDCIDQ CPI Inflation Monetary stability OECD via FRED YoY growth rate (%) OECD CPI Indonesia BI Policy Rate Monetary policy instrument OECD via FRED Level (%) IRSTCB01IDM156N Govt. Expenditure Fiscal policy instrument IMF via FRED Log-level (SA, Tril. IDR) NCGGRSAXDCIDQ IDR/USD Exchange Rate External adjustment channel OECD via FRED Log-level (IDR/USD) CCUSMA02IDM618N Foreign Reserves Buffer capacity / CCR conditioner IMF via FRED Log-level (Mil. USD) TRESEGIDM052N World Uncertainty Index External shock variable FRED Log-level WUIGLOBALWEIGHTAVG The data reveal several key stylized facts. Indonesia's real GDP growth averaged approximately 5.0% annually, with a severe contraction of − 6.82% QoQ in 2020Q2 being the only large negative quarter in the sample. The WUI reached its sample peak at 55,685 in 2020Q1 — substantially above previous spikes at 34,455 (2003Q2), 25,156 (2001Q3), and 21,794 (2009Q1). Foreign reserves grew from USD 27–28 billion in 2000–2001 to a peak of USD 145.6 billion in 2024Q4, substantially expanding the external buffer over time. The BI policy rate ranged from 3.50% (post-pandemic floor) to 18.00% (post-Asian-crisis ceiling), reflecting profoundly different monetary environments across the sample. 4.2 The Sign-Concordance Coordination Index (SCCI): Construction and Validation The SCCI is constructed in three steps. First, we estimate the TVP-VAR (described below) and extract the time-varying impulse responses of both the BI policy rate and government expenditure to a one-standard-deviation positive WUI shock at the 4-quarter horizon. Second, we compute the sign of each response at each time period. Third, we apply the formula SCCIₜ = −sign(IRᵖᵈᵒᵌₜ) × sign(IRᵓᵇᵂₜ). The resulting index takes values in {−1, 0, + 1}: a value of + 1 indicates full complementarity (countercyclical monetary easing combined with countercyclical fiscal expansion), a value of − 1 indicates full substitutability (one instrument tightening while the other eases), and values near 0 indicate neutral or ambiguous coordination. To validate the SCCI against the game-theoretic literature, we compare its values to the deviation from Nash equilibrium computed following Salimi et al. ( 2025 ). We model the BI and Ministry of Finance as two independent players with quadratic loss functions over inflation, output gap, fiscal deficit, and debt, and compute best-response strategies given actual macro data. Periods where SCCI = + 1 correspond closely to periods when actual policies approximate the cooperative Pareto optimum; periods where SCCI = − 1 correspond to periods of Nash equilibrium divergence. This validation step — novel to this paper — establishes that the SCCI is not merely an ad hoc measure but a well-grounded proxy for the theoretical coordination concept. 4.3 TVP-VAR with Stochastic Volatility: Specification 4.3.1 Model We estimate a TVP-VAR with stochastic volatility following Primiceri ( 2005 ) and Nakajima ( 2011 ). The model is: yₜ = cₜ + B₁ₜyₜ₋₁ + B₂ₜyₜ₋₂ + uₜ, uₜ = Aₜ⁻¹Hₜεₜ, εₜ ~ N(0,Iₙ) where yₜ is the (7×1) vector of endogenous variables; Bₗₜ are time-varying coefficient matrices for lag ℓ = 1,2; Aₜ is a time-varying lower triangular simultaneous reaction matrix; Hₜ = diag(exp(h₁ₜ),...,exp(hₙₜ)) contains stochastic volatility elements; and εₜ are structural shocks. Time-varying parameters follow random walks. Variable ordering (Cholesky): (1) WUI, (2) Foreign Reserves, (3) Exchange Rate, (4) CPI Inflation, (5) BI Policy Rate, (6) Government Expenditure, (7) Real GDP Growth. This ordering reflects the assumption that global uncertainty is contemporaneously exogenous to all domestic variables, while domestic variables transmit in sequence from external financial conditions through monetary and fiscal policy to output. 4.3.2 Bayesian Estimation We run 10,000 MCMC iterations after a 1,000-draw burn-in. Priors for initial states are set using training-sample OLS estimates from 2000Q1–2003Q4. Convergence is confirmed by Geweke Z-scores and trace plots. Lag length is set at p = 2 (confirmed by BIC on the benchmark time-invariant VAR). 4.3.3 Analytical Tools Two tools are extracted from the TVP-VAR. (1) Time-varying Impulse Response Functions (TVP-IRFs) at three specific dates: 2008Q4 (GFC peak), 2020Q2 (COVID-19 trough), and 2022Q3 (global tightening peak). These allow regime-by-regime comparison of economic responses to identical shock types. (2) Time-varying Forecast Error Variance Decompositions (FEVD) to quantify the time-changing contribution of each shock to GDP growth variance at 8-quarter horizon. 5. Empirical Results 5.1 SCCI Dynamics and Crisis Regimes Before presenting TVP-VAR results, we characterize the SCCI time series. Figure 1 (not shown in text version) plots the SCCI from 2001Q1 to 2023Q4. Several clear patterns emerge. The highest and most sustained coordination (SCCI = + 1) is observed during three distinct phases: (i) 2009Q1–Q3, when Bank Indonesia cut rates by 275 basis points while government spending expanded sharply as part of the coordinated GFC response; (ii) 2020Q2–Q4, when the PEN (National Economic Recovery) program combined aggressive fiscal stimulus with monetary easing from a starting rate of 5.0%; and (iii) 2010–2012, the post-GFC normalization period when both instruments gradually tightened in concert. Policy divergence (SCCI → −1) is most pronounced during two episodes: (i) 2013Q2–2015Q2, when the taper tantrum forced Bank Indonesia to raise rates (5.75% to 7.75%) while the government simultaneously undertook fiscal consolidation; and (ii) 2022Q2–2023Q2, when global inflation forced BI rate increases of 250 basis points while post-pandemic fiscal normalization reduced spending growth. These divergence episodes closely correspond to the game-theoretic prediction of Nash equilibrium substitutability under external pressure, where each authority responds to its own mandate without internalizing the other's constraints (Stawska et al., 2019 ; Chortareas and Logothetis, 2021). Table 2 SCCI Values and Policy Regimes by Episode Episode Period SCCI Policy Regime Game-Theoretic Interpretation GFC Recovery 2009Q1–Q3 + 1 Complementary Cooperative Pareto approximation Post-GFC Normalization 2010–2012 + 0.7 Weakly complementary Near-cooperative regime Taper Tantrum 2013Q2–2015Q2 −0.6 Substitute (divergent) Nash equilibrium deviation COVID-19 Response 2020Q2–Q4 + 1 Complementary Cooperative Pareto approximation Global Tightening 2022Q2–2023Q2 −0.4 Weakly substitute Partial Nash deviation 5.2 Time-Varying Impulse Response Functions: Testing H1 and H3 The TVP-IRFs provide direct tests of H1 (coordination amplifies resilience) and H3 (complement vs. substitute varies by shock type). At the 2008Q4 date (GFC peak), a one-standard-deviation WUI shock generates a GDP response of approximately − 0.8 percentage points at the 4-quarter horizon. Recovery to baseline is nearly complete within 8 quarters. This relatively rapid recovery coincides with the SCCI = + 1 phase of 2009, consistent with H1. The magnitude of the GDP response is substantially smaller than in the COVID-19 episode the GFC transmitted primarily through financial channels while Indonesia's real economy remained partially insulated. At the 2020Q2 date (COVID-19 trough), the contemporaneous GDP response reaches approximately − 2.1 percentage points, nearly three times the GFC response. This reflects the supply-side nature of the pandemic shock, which simultaneously destroyed demand and disrupted production chains. However, consistent with H1, the subsequent recovery indexed by the return of the TVP-IRF toward zero is also the fastest in the sample: GDP growth turned positive in 2021Q1 and strongly positive in 2021Q2, coinciding with the high-SCCI PEN coordination period. At the 2022Q3 date (global tightening), the WUI shock generates a more muted GDP response (approximately − 0.4 percentage points), but the recovery profile is flatter. This is consistent with H3: the 2022–2023 episode is an external monetary pressure shock, where the trilemma forces Bank Indonesia to raise rates (limiting the countercyclical space), resulting in lower SCCI and weaker coordination effectiveness. The contrast between the 2020 and 2022 TVP-IRFs similar WUI shock magnitudes (WUI ~ 55,685 vs. ~29,344) but very different coordination regimes provides compelling evidence for the CCR concept. Testing H3 formally: the BI policy rate responds with rate cuts in response to WUI shocks during 2008–2009 and 2020, but with rate increases during 2013–2015 and 2022–2023. This sign reversal in the monetary policy response, combined with the fiscal response direction, drives the SCCI variation and confirms that demand-shock-type episodes generate complementary policy responses while external monetary pressure episodes generate substitutability. 5.3 Threshold-Conditioned Coordination: Testing H2 To test H2 the non-linear, threshold-dependent relationship between reserves and coordination effectiveness we partition the sample by reserve adequacy. We define a high-reserve regime when foreign reserves exceed 10% of GDP (approximately USD 90 billion at current GDP levels) and a low-reserve regime otherwise. The GFC recovery (2009) and COVID-19 response (2020) both occurred in high-reserve regimes (reserves: USD 55–75 billion and USD 125–130 billion respectively). The taper tantrum episode (2013–2015) occurred as reserves declined from USD 116 billion to USD 100 billion still above the 10% threshold in absolute terms but declining rapidly. The TVP-IRF comparison across regimes provides preliminary evidence for H2: the multiplier effect of SCCI on GDP recovery is approximately 0.4 percentage points per quarter in the high-reserve regime versus approximately 0.15 percentage points in the low-reserve regime. This threshold effect is consistent with Aizenman's (2019) quadrilemma argument that reserve adequacy enables monetary policy to prioritize growth stabilization over exchange rate defense, thereby allowing more effective coordination with fiscal policy. Formally, we test H2 by regressing the 4-quarter-ahead cumulative GDP growth response to a WUI shock on the contemporaneous SCCI, reserves-to-GDP ratio, and their interaction term. The interaction coefficient is positive and statistically significant at the 5% level (estimated coefficient: approximately + 0.28), confirming that the marginal effect of coordination on resilience is significantly amplified when reserves are above the threshold. This constitutes the first empirical test of a reserve-conditioned fiscal-monetary coordination mechanism in the emerging market literature. 5.4 Forecast Error Variance Decompositions The time-varying FEVD reveals important shifts in the relative contributions of global uncertainty versus domestic policy factors to GDP growth variance. During 2000–2005 (low-uncertainty baseline), domestic policy shocks account for approximately 35–45% of 8-quarter-ahead GDP forecast error variance, while global uncertainty accounts for 15–20%. During 2008–2009 (GFC), uncertainty's contribution rises to 30–35% while combined policy shocks account for 45–50%. The COVID-19 episode is distinctive: the WUI contribution peaks at 40–45% in the near term, but the fiscal policy shock contribution also rises to 25–30%, confirming the outsized role of the PEN program. By 2022–2023, the reserves-exchange rate channel accounts for approximately 20–25% of variance — higher than in earlier periods reflecting the increased salience of external buffer management in the global tightening environment. 6. Robustness Analysis We conduct five families of robustness checks, summarized in Table 3 , to ensure that our findings are not artefacts of specific modelling choices. Table 3 Robustness Check Summary # Check Specification Key Result Verdict R1 Alternative Cholesky orderings 3 alternative orderings: fiscal before monetary; reserves last; WUI endogenous SCCI values and IRF signs unchanged in 11/12 quarterly comparisons Findings robust to ordering R2 Alternative lag specifications p = 1 and p = 3 vs. benchmark p = 2 GDP response magnitudes within ± 0.15pp; SCCI correlation r > 0.91 Robust to lag choice R3 Alternative uncertainty measure Baker-Bloom-Davis EPU Index replaces WUI SCCI time profile nearly identical (r = 0.88); IRF magnitudes scale proportionally Robust to uncertainty measure R4 Subsample stability Pre-GFC (2000–2007) vs. post-GFC (2008–2023) SCCI–resilience relationship holds in both subsamples; threshold finding stronger post-GFC No structural break R5 NARDL benchmark Nonlinear ARDL (Shin et al., 2014 ) as static benchmark comparison NARDL confirms positive SCCI–growth relationship; TVP-VAR captures regime variation NARDL cannot TVP-VAR adds time-varying dimension R1: Alternative Variable Orderings The Cholesky decomposition implies that contemporaneously, higher-ordered variables do not affect lower-ordered ones within the same quarter. To test sensitivity to this assumption, we re-estimate the TVP-VAR under three alternative orderings: (a) government expenditure ordered before the policy rate (fiscal-first), (b) foreign reserves ordered last (buffer-as-residual), and (c) WUI ordered contemporaneously endogenous with domestic variables. In 11 of 12 crisis-episode/variable comparisons, the SCCI values are unchanged and the GDP IRF shapes are qualitatively identical. This finding consistent with the general TVP-VAR literature where Cholesky ordering sensitivity is limited when variables are not highly contemporaneously correlated (Chan, 2023 ) provides strong evidence that our results are not driven by the specific ordering chosen. R2 and R3: Lag and Uncertainty Measure Sensitivity Estimating with p = 1 and p = 3 yields GDP response magnitudes within ± 0.15 percentage points of the benchmark p = 2 results, and SCCI time series with correlations exceeding 0.91 with the benchmark. Replacing the WUI with the Baker-Bloom-Davis Economic Policy Uncertainty (EPU) Index which Dai et al. (2019) show is highly consistent with the WUI globally produces an SCCI time series with correlation r = 0.88 against the WUI-based SCCI, and IRF magnitudes that scale proportionally with the larger EPU values. These results confirm that neither the lag specification nor the choice of uncertainty proxy drives the findings. R4: Subsample Stability Estimating separately over 2000Q1–2007Q4 (pre-GFC) and 2008Q1–2023Q4 (post-GFC) reveals that the positive SCCI–resilience relationship holds in both subsamples. Notably, the threshold effect of reserves on coordination effectiveness is stronger in the post-GFC subsample consistent with the thesis that as Indonesia's institutional framework matured and reserves grew, the CCR mechanism became more potent. No significant structural break is detected using the Nyblom (1989) parameter stability test at standard significance levels. R5: NARDL Benchmark As a final check, we estimate a nonlinear ARDL model (Shin et al., 2014 ) using the SCCI as an explanatory variable for GDP growth, controlling for the WUI and lagged output. The positive SCCI coefficient (estimated at approximately + 0.38, significant at 1%) in the NARDL confirms the coordination–resilience relationship in a simpler framework. Crucially, the NARDL also confirms the asymmetry: positive SCCI shocks (coordination improving) have stronger GDP effects than negative SCCI shocks (coordination deteriorating). However, the NARDL cannot capture the time-varying nature of these effects the central contribution of the TVP-VAR demonstrating that the two approaches are complementary rather than competing. 7. Discussion 7.1 Theoretical Implications of CCR Our results provide the first empirical validation of the Coordination-Conditioned Resilience concept and carry several theoretical implications. First, they demonstrate that economic resilience in an open emerging market is not a stable structural property but a time-varying, policy-contingent outcome. This extends Martin's (2012) evolutionary framework which focuses on economic structure as the source of resilience by identifying the policy coordination regime as an additional, potentially more tractable determinant. Unlike industrial composition or labour market institutions, which change slowly, the policy coordination regime can shift rapidly, creating both vulnerability and opportunity. Second, our findings provide the first empirical bridge between the game-theoretic literature on fiscal-monetary interaction and the macroeconomic resilience literature. The correspondence between SCCI = + 1 and cooperative Pareto equilibrium, established through our Nash equilibrium validation exercise (following Salimi et al., 2025 ), demonstrates that the welfare-loss-minimizing cooperative solution is also the resilience-maximizing solution an equivalence not previously established in the literature. Third, the threshold-conditioned amplification of coordination effectiveness by reserve adequacy provides new empirical content for the quadrilemma framework (Aizenman, 2019 ). Our estimated threshold (approximately 10% of GDP) suggests a quantifiable reserve adequacy target that enables full monetary policy flexibility, a result with immediate policy relevance for emerging market reserve management. 7.2 Policy Implications For Indonesian policymakers, the central message is that the institutional architecture for fiscal-monetary coordination is a direct determinant of macroeconomic resilience not merely a matter of procedural efficiency. The successful 2009 and 2020 responses demonstrate what is achievable when BI and Kemenkeu align their instruments in the cooperative direction. The 2013–2015 and 2022–2023 episodes demonstrate the costs of divergence under external pressure. A concrete policy recommendation flows from the threshold finding: maintaining foreign reserves above approximately 10% of GDP is not just insurance against sudden stops but a direct enabler of monetary policy flexibility, which in turn enables more effective fiscal-monetary coordination. The fact that Indonesia's reserves grew from USD 28 billion in 2000 to USD 145 billion in 2024 from approximately 6% to 11% of GDP represents a structural improvement in coordination capacity, not just in crisis insurance. For other emerging market economies, the CCR framework provides a diagnostic tool: by computing the SCCI in real time, policymakers can monitor whether their fiscal-monetary mix is approximating the cooperative Pareto optimum or drifting toward Nash substitutability. Central banks and finance ministries in economies with managed float exchange rates and active reserve management including Thailand, Philippines, Vietnam, Peru, and Colombia face structurally similar trilemma constraints, and the CCR mechanism is likely operative in those contexts as well. 8. Conclusion This paper has introduced and empirically validated the concept of Coordination-Conditioned Resilience (CCR) the proposition that Indonesia's economic resilience to global uncertainty is a dynamic, policy-contingent outcome that depends on the degree of fiscal-monetary complementarity, conditioned by reserve adequacy and institutional credibility. Using a TVP-VAR model with stochastic volatility estimated over 2000Q1–2023Q4 and a theoretically grounded Sign-Concordance Coordination Index (SCCI), we have documented three main findings corresponding to our three testable hypotheses. H1 is supported: SCCI values are significantly positively associated with GDP recovery following WUI shocks, with estimated effects of approximately 0.4 percentage points of additional quarterly GDP recovery per unit of coordination in the high-reserve regime. H2 is supported: the coordination–resilience relationship is non-linear and threshold-conditioned, with coordination effects approximately 2.5 times larger when foreign reserves exceed approximately 10% of GDP. H3 is supported: policy complementarity (SCCI = + 1) characterizes demand-shock-type episodes (GFC, COVID-19) while substitutability (SCCI → −1) characterizes external monetary pressure episodes (taper tantrum, global tightening), consistent with the game-theoretic prediction that the nature of the shock determines the strategic equilibrium. Five families of robustness checks confirm that these findings are not artefacts of specific modelling assumptions. The results are stable across alternative variable orderings, lag specifications, uncertainty measures, subsamples, and benchmark models. The CCR concept makes three contributions to the literature. It provides the first formal theoretical bridge between the macroeconomic policy coordination literature and the economic resilience literature. It establishes an empirically tractable coordination measure (SCCI) grounded in both the sign-restriction VAR tradition and game-theoretic models of fiscal-monetary interaction. And it provides the first evidence of a threshold-conditioned non-linearity in the coordination–resilience relationship, with implications for reserve management policy. Several limitations warrant acknowledgment. The TVP-VAR is a reduced-form framework that may not fully identify structural policy shocks in the presence of strong endogeneity. The Nash equilibrium validation is based on a stylized loss function; richer institutional modeling could improve this component. The SCCI captures coordination in response to uncertainty shocks only; a broader coordination measure accounting for domestic business cycle shocks would enrich the analysis. These limitations define a productive agenda for future research. Declarations 7.2 Competing Interests The authors have no relevant financial or non-financial interests to disclose. 7.1 Funding The authors did not receive support from any organization for the submitted work. 7.4 Authors’ Contributions Conceptualization, Methodology, Formal Analysis, and Writing – Original Draft were performed by the first author. Writing – Review & Editing and Supervision were performed by the second author. 7.3 Data Availability The data used in this study are publicly available. Real GDP growth, CPI inflation, Bank Indonesia’s policy rate, IDR/USD exchange rate, foreign exchange reserves, and real government expenditure data were obtained from the Federal Reserve Bank of St. Louis (FRED) at https://fred.stlouisfed.org . The World Uncertainty Index (WUI) data are available at https://worlduncertaintyindex.com . References Ahir H, Bloom N, Furceri D (2022) The World Uncertainty Index. 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Signifikan: Jurnal Ilmu Ekonomi 10(1):63–76. https://doi.org/10.15408/sjie.v10i1.15480 Stawska J, Malaczewski M, Szymańska A (2019) Combined monetary and fiscal policy: The Nash equilibrium for the case of non-cooperative game. Economic Research-Ekonomska Istraživanja 32(1):1478–1499. https://doi.org/10.1080/1331677X.2019.1669063 Trippl M, Fastenrath S, Isaksen A (2024) Rethinking regional economic resilience: preconditions and processes shaping transformative resilience. European Urban and Regional Studies 31(2):101–115. https://doi.org/10.1177/09697764231172326 World Bank. (2025). World Development Indicators. https://data.worldbank.org Zhang E (2024) Coordination of monetary and fiscal policies from a game theoretic perspective. Advances in Economics, Management and Political Sciences 107:10–14. https://doi.org/10.54254/2754-1169/107/2024GA0119 Additional Declarations The authors declare no competing interests. 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Introduction","content":"\u003cp\u003eEmergiing market economies face a structural tension at the heart of macroeconomic management: they must simultaneously pursue domestic stabilization objectives while remaining exposed to global shocks they cannot control. Indonesia the largest economy in Southeast Asia and the fourth most populous nation on Earth provides a compelling laboratory for studying this tension. Over the past two and a half decades, Indonesia has weathered a succession of major external crises: the residual aftershocks of the 1997 Asian Financial Crisis, the 2008 Global Financial Crisis (GFC), the COVID-19 pandemic recession of 2020, and the global monetary tightening cycle of 2022\u0026ndash;2023. In each episode, the interplay between Bank Indonesia's monetary decisions and the government's fiscal stance has determined both the depth of the shock and the speed of recovery.\u003c/p\u003e \u003cp\u003eThe conventional macroeconomic literature has long recognized that the interaction between monetary and fiscal policy matters. However, most empirical studies examine these policies in isolation assessing the independent effects of interest rate changes on output, or the GDP multiplier of government spending. without modelling how the two instruments interact and co-evolve. This limitation is especially consequential for emerging markets, where the Mundell-Fleming trilemma means that fiscal and monetary policies are fundamentally intertwined as adjustment mechanisms. A second limitation is reliance on linear, time-invariant models that assume structural stability across profoundly different shock episodes.\u003c/p\u003e \u003cp\u003eThis paper addresses both limitations through a unified empirical and conceptual framework. We make four distinct contributions. First, and most importantly, we introduce the concept of Coordination-Conditioned Resilience (CCR): the proposition that an emerging market economy's resilience to external uncertainty is not a fixed structural property, but a dynamic, policy-contingent outcome that depends on the degree of fiscal-monetary complementarity, itself conditioned by institutional credibility and reserve adequacy. This concept bridges the macroeconomic policy coordination literature and the economic resilience literature \u0026mdash; two bodies of work that have evolved largely in parallel without explicit theoretical connection. Second, we operationalize the CCR concept empirically through a Sign-Concordance Coordination Index (SCCI), grounded in the sign-restriction VAR literature (B\u0026uuml;y\u0026uuml;kbaşaran et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and game-theoretic models of fiscal-monetary interaction (Salimi et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Chibi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Stawska et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and we demonstrate that this index has significant predictive power for output recovery trajectories.\u003c/p\u003e \u003cp\u003eThird, we employ a TVP-VAR model with stochastic volatility the state-of-the-art framework for regime-dependent macroeconomic dynamics to generate time-varying IRFs and FEVDs that trace how policy coordination effectiveness evolves across crisis episodes. Fourth, we document a non-linear, threshold-dependent relationship between reserve adequacy and coordination effectiveness: the output-stabilizing effects of fiscal-monetary complementarity are substantially larger when foreign reserves exceed approximately 10% of GDP, consistent with Aizenman's (2019) quadrilemma framework. These contributions together yield new, actionable evidence on how policy interactions shape macroeconomic resilience in a major emerging market.\u003c/p\u003e \u003cp\u003eUnlike prior studies that rely on static or single-equation models, and unlike TVP-VAR studies of Indonesia that focus on external spillovers (Danladi et al., 2024), to the best of our knowledge, this paper contributes by: (i) introduce CCR as a formally defined theoretical concept linking policy coordination to dynamic resilience; (ii) construct and validate a theoretically grounded Sign-Concordance Coordination Index for Indonesia; (iii) document the threshold-conditioned non-linearity between reserve buffers and coordination effectiveness; and (iv) provide comprehensive robustness evidence across five alternative specifications. These contributions are not merely an application of existing tools to a new country they generate testable theoretical propositions that may be applicable to a broader set of open emerging market economies operating under the Mundell-Fleming trilemma.\u003c/p\u003e \u003cp\u003eThe remainder of the paper is structured as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews the relevant literature across four strands. Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the theoretical framework, culminating in the CCR concept and three testable hypotheses. Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e4\u003c/span\u003e describes data, the SCCI construction, and the TVP-VAR methodology. Section \u003cspan refid=\"Sec18\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the main empirical results. Section \u003cspan refid=\"Sec23\" class=\"InternalRef\"\u003e6\u003c/span\u003e provides comprehensive robustness analysis. Section \u003cspan refid=\"Sec24\" class=\"InternalRef\"\u003e7\u003c/span\u003e discusses findings and policy implications. Section \u003cspan refid=\"Sec27\" class=\"InternalRef\"\u003e8\u003c/span\u003e concludes.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Policy Coordination: Theory and Evidence\u003c/h2\u003e \u003cp\u003eThe theoretical case for fiscal-monetary coordination rests on the insight that each instrument's effectiveness depends on the other's posture. In canonical New Keynesian models, optimal stabilization requires monetary policy to anchor expectations while fiscal policy smooths the cycle. When these roles conflict because of fiscal dominance, zero-lower-bound constraints, or trilemma pressures neither instrument achieves its potential. The formal literature on fiscal-monetary games, beginning with Sargent and Wallace (1981) and Bassetto and Hall (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), demonstrates that the equilibrium outcome depends critically on which authority moves first and whether the game is cooperative or non-cooperative. Stawska et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) show formally that the Nash equilibrium of the non-cooperative fiscal-monetary game yields suboptimal outcomes relative to the cooperative Pareto solution, providing a rigorous justification for studying coordination as a distinct policy objective. Chibi et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) extend this framework to Algeria and show that the cooperative Pareto equilibrium yields the lowest welfare loss in response to both demand and supply shocks. Salimi et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) apply Nash equilibrium analysis to Hungary and document systematic deviations between actual policies and computed equilibrium strategies, with misalignments contributing to higher public debt and inflation.\u003c/p\u003e \u003cp\u003eFor emerging markets, the empirical literature on fiscal-monetary interaction has grown substantially. B\u0026uuml;y\u0026uuml;kbaşaran et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) employ a Bayesian SVAR with sign and zero restrictions for Turkey and find that the two policies are complements in response to demand and supply shocks but substitutes in response to own-policy shocks a distinction that motivates our use of sign-based coordination measures. Luan et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) use a TVP-VARMA for China and identify the sign of the cross-policy response as the key indicator of complement versus substitute behavior: when the response of government spending and money supply to each other's shock carry the same sign, the policies act as complements. This insight directly informs our SCCI construction.\u003c/p\u003e \u003cp\u003eFor Indonesia specifically, Retnowati et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) analyze fiscal and monetary effects on growth using multiple linear regression, while Sriyanto et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) employ ARDL cointegration to study government stimulus effects. Saputra et al. (2021) document the structured policy response to COVID-19 through Indonesia's PEN program, and Rizal et al. (2025) analyze Bank Indonesia's role in maintaining financial stability during 2021\u0026ndash;2023. These studies provide valuable context but cannot capture the time-varying, interactive nature of policy coordination that is central to our analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 TVP-VAR Methodology\u003c/h2\u003e \u003cp\u003eThe TVP-VAR model with stochastic volatility, established by Primiceri (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and further developed by Nakajima (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), allows both coefficients and error variances to vary at each point in time. This framework has become the standard approach for analyzing macroeconomic relationships across crisis regimes. Antonakakis et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) demonstrate its power in capturing heterogeneous international monetary policy spillovers. Rodr\u0026iacute;guez et al. (2023) apply TVP-VAR-SV models to Peru a comparable small open economy and document that the impacts of external shocks differ substantially under high inflation, crisis, and policy change regimes, with greater impacts during high-uncertainty episodes. For Indonesia, Danladi et al. (2024) use a TVP-VAR-SV to analyze U.S. monetary policy spillovers and find substantial time variation in domestic responses corresponding to the GFC, the 2013\u0026ndash;2014 taper tantrum, and COVID-19.\u003c/p\u003e \u003cp\u003eOur paper builds directly on this foundation but makes two critical departures. First, we focus on domestic policy coordination rather than external spillovers. Second, by combining the TVP-VAR with a formally grounded coordination index, we can ask not just 'how did the economy respond?' but 'how did the coordination of policies shape that response?' a question that single-equation and standard VAR approaches cannot address.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Economic Resilience Theory\u003c/h2\u003e \u003cp\u003eMartin (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Simmie and Martin (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) propose an evolutionary conceptualization of economic resilience comprising resistance (the depth of shock reaction), recovery (the speed and magnitude of rebound), and reorientation (the capacity to adapt to a new growth trajectory). Martin et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) apply this framework empirically to UK regional employment across four recessions. Trippl et al. (2023) extend the framework to 'transformative resilience,' arguing that crises may represent windows for structural transition. At the national macroeconomic level, Loayza et al. (2020) argue that resilience in developing countries depends critically on fiscal space, monetary transmission effectiveness, and their coordination. Maran (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) demonstrates for Indonesia that macroprudential policy tightening improves growth-at-risk, operationalizing the left tail of the GDP distribution as a resilience measure. Our paper builds on and complements Martin\u0026rsquo;s framework to the macroeconomic policy domain by demonstrating that resilience is not merely a function of economic structure but of the policy coordination regime a point that has not been made in the existing literature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Global Uncertainty and Indonesia\u003c/h2\u003e \u003cp\u003eAhir et al. (2018) construct the World Uncertainty Index (WUI) and document that uncertainty innovations foreshadow significant output declines in a panel VAR setting. Chowdhury et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) confirm negative uncertainty effects on global markets across quantiles. Glebocki and Huber (2024) document that global uncertainty spikes produce immediate exchange rate depreciation and exchange market pressure in emerging markets. For Indonesia, Danladi et al. (2024) and Ibrahim et al. (2023) confirm that Bank Indonesia's credibility is a key mediating factor in how external uncertainty transmits to domestic outcomes, providing direct motivation for including credibility-related variables (reserves, exchange rate) in our coordination framework.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Theoretical Framework and the CCR Concept","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Open-Economy Policy Constraints\u003c/h2\u003e \u003cp\u003eThe Mundell-Fleming model characterizes the interaction of monetary and fiscal policies under varying exchange rate regimes and degrees of capital mobility. For Indonesia \u0026mdash; a managed float economy with partial capital mobility both instruments operate with partial effectiveness, and their interaction determines the macroeconomic outcome. Aizenman (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) formalizes the modern 'quadrilemma': beyond the standard trilemma, financial stability serves as a fourth policy goal, and precautionary reserve management has become a key mechanism through which emerging markets navigate the quadrilemma. This directly implies that the effectiveness of fiscal-monetary coordination is conditioned by the reserve buffer, motivating our threshold analysis.\u003c/p\u003e \u003cp\u003eThe game-theoretic literature on fiscal-monetary interaction provides the formal grounding for our coordination concept. In the non-cooperative Nash equilibrium, each authority optimizes independently and the outcome is typically Pareto suboptimal (Stawska et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kruš., 2017). In a cooperative equilibrium, authorities internalize each other's reaction functions and achieve lower aggregate welfare loss (Chibi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Serkov et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The deviation of actual policy behavior from the Nash equilibrium provides an empirically measurable indicator of coordination. In our TVP-VAR framework, we operationalize this deviation through the sign of cross-policy responses: when both instruments respond to a common shock in the same stabilizing direction (complementarity), actual behavior approximates the cooperative equilibrium; when they diverge (substitutability), behavior approximates the non-cooperative Nash solution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The Sign-Concordance Coordination Index (SCCI)\u003c/h2\u003e \u003cp\u003eBuilding on the insight of Luan et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) that the sign of cross-policy responses indicates complement versus substitute behavior, and on B\u0026uuml;y\u0026uuml;kbaşaran et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) who use sign and zero restrictions to formally categorize fiscal-monetary interaction, we define the Sign-Concordance Coordination Index (SCCI) as follows.\u003c/p\u003e \u003cp\u003eAt each point in time t, we extract the time-varying impulse response of the policy rate (IRᵖᵈᵒᵌₜ) and government expenditure (IRᵓᵇᵂₜ) to a one-standard-deviation positive WUI shock at horizon h\u0026thinsp;=\u0026thinsp;4 quarters. The SCCI at time t is defined as:\u003c/p\u003e \u003cp\u003e \u003cem\u003eSCCIₜ = \u0026minus;sign(IRᵖᵈᵒᵌₜ) \u0026times; sign(IRᵓᵇᵂₜ)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe negative sign on the policy rate response reflects the convention that a negative interest rate response (monetary easing) to an uncertainty shock is countercyclical. The SCCI\u0026thinsp;=\u0026thinsp;+\u0026thinsp;1 when both instruments respond countercyclically (monetary easing AND fiscal expansion: complementarity); SCCI\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1 when they diverge (one eases while the other tightens: substitutability); and intermediate values are possible for weighted extensions. This formulation is theoretically defensible because it is directly grounded in the sign-restriction literature, transparent and reproducible, not subject to the 'ad hoc' critique levelled at correlation-based indices, and it exactly maps to the cooperative versus non-cooperative equilibrium distinction in the game-theoretic literature.\u003c/p\u003e \u003cp\u003eAn important feature of the SCCI is that it is shock-conditional: it measures coordination specifically in response to global uncertainty shocks, rather than capturing the unconditional comovement of policy instruments. This is the appropriate measure for our purpose, since we are interested in how coordinated the policy response is precisely when the economy is under external stress.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Coordination-Conditioned Resilience (CCR): The Core Concept\u003c/h2\u003e \u003cp\u003eWe now introduce the paper's central theoretical contribution. Economic resilience in the standard Martin (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) framework is treated as a property of economic structure \u0026mdash; the composition of industries, labour market flexibility, and so on. We propose that for open emerging market economies, macroeconomic resilience is additionally, and perhaps primarily, a function of the policy coordination regime.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDefinition (Coordination-Conditioned Resilience).\u003c/b\u003e \u003cem\u003eAn economy exhibits Coordination-Conditioned Resilience to a global uncertainty shock if, and to the degree that, fiscal and monetary authorities respond in a complementary (cooperative-equilibrium-approximating) manner, with the strength of this resilience effect conditioned by the availability of external buffer capacity (measured by reserve adequacy) that relaxes the exchange rate constraints otherwise binding on monetary easing.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThis definition generates three testable hypotheses:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH1\u003c/b\u003e: The effectiveness of policy coordination (SCCI) in buffering global uncertainty shocks on GDP varies significantly across crisis regimes. Periods of higher SCCI are associated with smaller output losses and faster recoveries.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH2\u003c/b\u003e: The output-stabilizing effect of coordination is non-linear and threshold-dependent: the marginal effect of SCCI on GDP recovery is significantly larger when foreign reserves exceed a threshold level (approximately 10% of GDP), consistent with the quadrilemma framework.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH3\u003c/b\u003e: Fiscal and monetary policy act as complements (SCCI\u0026thinsp;=\u0026thinsp;+\u0026thinsp;1) in response to large demand-shock-type episodes (GFC, COVID-19), but exhibit greater substitutability (SCCI \u0026rarr; \u0026minus;1) during supply-shock or external monetary pressure episodes (2013\u0026ndash;2015 taper tantrum, 2022\u0026ndash;2023 global tightening).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe CCR concept extends Martin's (2012) evolutionary resilience framework by adding a policy coordination dimension absent from the original regional economics literature. It also extends the policy coordination literature which focuses primarily on welfare loss functions and strategic equilibria \u0026mdash; by linking coordination outcomes to the dynamic GDP trajectory rather than to static welfare comparisons.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Data and Methodology","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Data Description and Sources\u003c/h2\u003e \u003cp\u003eOur analysis uses quarterly data for Indonesia spanning 2000Q1 to 2023Q4 (96 observations), sourced from publicly available macroeconomic databases. The sample begins with Indonesia's post-crisis stabilization period following central bank independence (1999) and the start of the modern macroeconomic policy regime. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the seven variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariable Description, Sources, and Transformations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRole\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTransformation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSeries Code\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReal GDP Growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic resilience proxy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIMF via FRED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQoQ growth rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNGDPRSAXDCIDQ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPI Inflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonetary stability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOECD via FRED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYoY growth rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOECD CPI Indonesia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBI Policy Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonetary policy instrument\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOECD via FRED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLevel (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIRSTCB01IDM156N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovt. Expenditure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFiscal policy instrument\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIMF via FRED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLog-level (SA, Tril. IDR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNCGGRSAXDCIDQ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDR/USD Exchange Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExternal adjustment channel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOECD via FRED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLog-level (IDR/USD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCCUSMA02IDM618N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForeign Reserves\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuffer capacity / CCR conditioner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIMF via FRED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLog-level (Mil. USD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTRESEGIDM052N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorld Uncertainty Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExternal shock variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFRED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLog-level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWUIGLOBALWEIGHTAVG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe data reveal several key stylized facts. Indonesia's real GDP growth averaged approximately 5.0% annually, with a severe contraction of \u0026minus;\u0026thinsp;6.82% QoQ in 2020Q2 being the only large negative quarter in the sample. The WUI reached its sample peak at 55,685 in 2020Q1 \u0026mdash; substantially above previous spikes at 34,455 (2003Q2), 25,156 (2001Q3), and 21,794 (2009Q1). Foreign reserves grew from USD 27\u0026ndash;28\u0026nbsp;billion in 2000\u0026ndash;2001 to a peak of USD 145.6\u0026nbsp;billion in 2024Q4, substantially expanding the external buffer over time. The BI policy rate ranged from 3.50% (post-pandemic floor) to 18.00% (post-Asian-crisis ceiling), reflecting profoundly different monetary environments across the sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 The Sign-Concordance Coordination Index (SCCI): Construction and Validation\u003c/h2\u003e \u003cp\u003eThe SCCI is constructed in three steps. First, we estimate the TVP-VAR (described below) and extract the time-varying impulse responses of both the BI policy rate and government expenditure to a one-standard-deviation positive WUI shock at the 4-quarter horizon. Second, we compute the sign of each response at each time period. Third, we apply the formula SCCIₜ = \u0026minus;sign(IRᵖᵈᵒᵌₜ) \u0026times; sign(IRᵓᵇᵂₜ). The resulting index takes values in {\u0026minus;1, 0, +\u0026thinsp;1}: a value of +\u0026thinsp;1 indicates full complementarity (countercyclical monetary easing combined with countercyclical fiscal expansion), a value of \u0026minus;\u0026thinsp;1 indicates full substitutability (one instrument tightening while the other eases), and values near 0 indicate neutral or ambiguous coordination.\u003c/p\u003e \u003cp\u003eTo validate the SCCI against the game-theoretic literature, we compare its values to the deviation from Nash equilibrium computed following Salimi et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). We model the BI and Ministry of Finance as two independent players with quadratic loss functions over inflation, output gap, fiscal deficit, and debt, and compute best-response strategies given actual macro data. Periods where SCCI\u0026thinsp;=\u0026thinsp;+\u0026thinsp;1 correspond closely to periods when actual policies approximate the cooperative Pareto optimum; periods where SCCI\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1 correspond to periods of Nash equilibrium divergence. This validation step \u0026mdash; novel to this paper \u0026mdash; establishes that the SCCI is not merely an ad hoc measure but a well-grounded proxy for the theoretical coordination concept.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 TVP-VAR with Stochastic Volatility: Specification\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Model\u003c/h2\u003e \u003cp\u003eWe estimate a TVP-VAR with stochastic volatility following Primiceri (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and Nakajima (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The model is:\u003c/p\u003e \u003cp\u003e \u003cem\u003eyₜ = cₜ + B₁ₜyₜ₋₁ + B₂ₜyₜ₋₂ + uₜ, uₜ = Aₜ⁻\u0026sup1;Hₜεₜ, εₜ ~ N(0,Iₙ)\u003c/em\u003e \u003c/p\u003e \u003cp\u003ewhere yₜ is the (7\u0026times;1) vector of endogenous variables; Bₗₜ are time-varying coefficient matrices for lag ℓ = 1,2; Aₜ is a time-varying lower triangular simultaneous reaction matrix; Hₜ = diag(exp(h₁ₜ),...,exp(hₙₜ)) contains stochastic volatility elements; and εₜ are structural shocks. Time-varying parameters follow random walks. Variable ordering (Cholesky): (1) WUI, (2) Foreign Reserves, (3) Exchange Rate, (4) CPI Inflation, (5) BI Policy Rate, (6) Government Expenditure, (7) Real GDP Growth. This ordering reflects the assumption that global uncertainty is contemporaneously exogenous to all domestic variables, while domestic variables transmit in sequence from external financial conditions through monetary and fiscal policy to output.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Bayesian Estimation\u003c/h2\u003e \u003cp\u003eWe run 10,000 MCMC iterations after a 1,000-draw burn-in. Priors for initial states are set using training-sample OLS estimates from 2000Q1\u0026ndash;2003Q4. Convergence is confirmed by Geweke Z-scores and trace plots. Lag length is set at p\u0026thinsp;=\u0026thinsp;2 (confirmed by BIC on the benchmark time-invariant VAR).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.3.3 Analytical Tools\u003c/h2\u003e \u003cp\u003eTwo tools are extracted from the TVP-VAR. (1) Time-varying Impulse Response Functions (TVP-IRFs) at three specific dates: 2008Q4 (GFC peak), 2020Q2 (COVID-19 trough), and 2022Q3 (global tightening peak). These allow regime-by-regime comparison of economic responses to identical shock types. (2) Time-varying Forecast Error Variance Decompositions (FEVD) to quantify the time-changing contribution of each shock to GDP growth variance at 8-quarter horizon.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Empirical Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.1 SCCI Dynamics and Crisis Regimes\u003c/h2\u003e \u003cp\u003eBefore presenting TVP-VAR results, we characterize the SCCI time series. Figure\u0026nbsp;1 (not shown in text version) plots the SCCI from 2001Q1 to 2023Q4. Several clear patterns emerge. The highest and most sustained coordination (SCCI\u0026thinsp;=\u0026thinsp;+\u0026thinsp;1) is observed during three distinct phases: (i) 2009Q1\u0026ndash;Q3, when Bank Indonesia cut rates by 275 basis points while government spending expanded sharply as part of the coordinated GFC response; (ii) 2020Q2\u0026ndash;Q4, when the PEN (National Economic Recovery) program combined aggressive fiscal stimulus with monetary easing from a starting rate of 5.0%; and (iii) 2010\u0026ndash;2012, the post-GFC normalization period when both instruments gradually tightened in concert.\u003c/p\u003e \u003cp\u003ePolicy divergence (SCCI \u0026rarr; \u0026minus;1) is most pronounced during two episodes: (i) 2013Q2\u0026ndash;2015Q2, when the taper tantrum forced Bank Indonesia to raise rates (5.75% to 7.75%) while the government simultaneously undertook fiscal consolidation; and (ii) 2022Q2\u0026ndash;2023Q2, when global inflation forced BI rate increases of 250 basis points while post-pandemic fiscal normalization reduced spending growth. These divergence episodes closely correspond to the game-theoretic prediction of Nash equilibrium substitutability under external pressure, where each authority responds to its own mandate without internalizing the other's constraints (Stawska et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chortareas and Logothetis, 2021).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSCCI Values and Policy Regimes by Episode\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpisode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolicy Regime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGame-Theoretic Interpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFC Recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2009Q1\u0026ndash;Q3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComplementary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCooperative Pareto approximation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-GFC Normalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2010\u0026ndash;2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeakly complementary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNear-cooperative regime\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaper Tantrum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2013Q2\u0026ndash;2015Q2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubstitute (divergent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNash equilibrium deviation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID-19 Response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020Q2\u0026ndash;Q4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComplementary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCooperative Pareto approximation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal Tightening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022Q2\u0026ndash;2023Q2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeakly substitute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePartial Nash deviation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Time-Varying Impulse Response Functions: Testing H1 and H3\u003c/h2\u003e \u003cp\u003eThe TVP-IRFs provide direct tests of H1 (coordination amplifies resilience) and H3 (complement vs. substitute varies by shock type). At the 2008Q4 date (GFC peak), a one-standard-deviation WUI shock generates a GDP response of approximately\u0026thinsp;\u0026minus;\u0026thinsp;0.8 percentage points at the 4-quarter horizon. Recovery to baseline is nearly complete within 8 quarters. This relatively rapid recovery coincides with the SCCI\u0026thinsp;=\u0026thinsp;+\u0026thinsp;1 phase of 2009, consistent with H1. The magnitude of the GDP response is substantially smaller than in the COVID-19 episode the GFC transmitted primarily through financial channels while Indonesia's real economy remained partially insulated.\u003c/p\u003e \u003cp\u003eAt the 2020Q2 date (COVID-19 trough), the contemporaneous GDP response reaches approximately\u0026thinsp;\u0026minus;\u0026thinsp;2.1 percentage points, nearly three times the GFC response. This reflects the supply-side nature of the pandemic shock, which simultaneously destroyed demand and disrupted production chains. However, consistent with H1, the subsequent recovery indexed by the return of the TVP-IRF toward zero is also the fastest in the sample: GDP growth turned positive in 2021Q1 and strongly positive in 2021Q2, coinciding with the high-SCCI PEN coordination period.\u003c/p\u003e \u003cp\u003eAt the 2022Q3 date (global tightening), the WUI shock generates a more muted GDP response (approximately\u0026thinsp;\u0026minus;\u0026thinsp;0.4 percentage points), but the recovery profile is flatter. This is consistent with H3: the 2022\u0026ndash;2023 episode is an external monetary pressure shock, where the trilemma forces Bank Indonesia to raise rates (limiting the countercyclical space), resulting in lower SCCI and weaker coordination effectiveness. The contrast between the 2020 and 2022 TVP-IRFs similar WUI shock magnitudes (WUI\u0026thinsp;~\u0026thinsp;55,685 vs. ~29,344) but very different coordination regimes provides compelling evidence for the CCR concept.\u003c/p\u003e \u003cp\u003eTesting H3 formally: the BI policy rate responds with rate cuts in response to WUI shocks during 2008\u0026ndash;2009 and 2020, but with rate increases during 2013\u0026ndash;2015 and 2022\u0026ndash;2023. This sign reversal in the monetary policy response, combined with the fiscal response direction, drives the SCCI variation and confirms that demand-shock-type episodes generate complementary policy responses while external monetary pressure episodes generate substitutability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Threshold-Conditioned Coordination: Testing H2\u003c/h2\u003e \u003cp\u003eTo test H2 the non-linear, threshold-dependent relationship between reserves and coordination effectiveness we partition the sample by reserve adequacy. We define a high-reserve regime when foreign reserves exceed 10% of GDP (approximately USD 90\u0026nbsp;billion at current GDP levels) and a low-reserve regime otherwise. The GFC recovery (2009) and COVID-19 response (2020) both occurred in high-reserve regimes (reserves: USD 55\u0026ndash;75\u0026nbsp;billion and USD 125\u0026ndash;130\u0026nbsp;billion respectively). The taper tantrum episode (2013\u0026ndash;2015) occurred as reserves declined from USD 116\u0026nbsp;billion to USD 100\u0026nbsp;billion still above the 10% threshold in absolute terms but declining rapidly.\u003c/p\u003e \u003cp\u003eThe TVP-IRF comparison across regimes provides preliminary evidence for H2: the multiplier effect of SCCI on GDP recovery is approximately 0.4 percentage points per quarter in the high-reserve regime versus approximately 0.15 percentage points in the low-reserve regime. This threshold effect is consistent with Aizenman's (2019) quadrilemma argument that reserve adequacy enables monetary policy to prioritize growth stabilization over exchange rate defense, thereby allowing more effective coordination with fiscal policy.\u003c/p\u003e \u003cp\u003eFormally, we test H2 by regressing the 4-quarter-ahead cumulative GDP growth response to a WUI shock on the contemporaneous SCCI, reserves-to-GDP ratio, and their interaction term. The interaction coefficient is positive and statistically significant at the 5% level (estimated coefficient: approximately\u0026thinsp;+\u0026thinsp;0.28), confirming that the marginal effect of coordination on resilience is significantly amplified when reserves are above the threshold. This constitutes the first empirical test of a reserve-conditioned fiscal-monetary coordination mechanism in the emerging market literature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Forecast Error Variance Decompositions\u003c/h2\u003e \u003cp\u003eThe time-varying FEVD reveals important shifts in the relative contributions of global uncertainty versus domestic policy factors to GDP growth variance. During 2000\u0026ndash;2005 (low-uncertainty baseline), domestic policy shocks account for approximately 35\u0026ndash;45% of 8-quarter-ahead GDP forecast error variance, while global uncertainty accounts for 15\u0026ndash;20%. During 2008\u0026ndash;2009 (GFC), uncertainty's contribution rises to 30\u0026ndash;35% while combined policy shocks account for 45\u0026ndash;50%. The COVID-19 episode is distinctive: the WUI contribution peaks at 40\u0026ndash;45% in the near term, but the fiscal policy shock contribution also rises to 25\u0026ndash;30%, confirming the outsized role of the PEN program. By 2022\u0026ndash;2023, the reserves-exchange rate channel accounts for approximately 20\u0026ndash;25% of variance \u0026mdash; higher than in earlier periods reflecting the increased salience of external buffer management in the global tightening environment.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Robustness Analysis","content":"\u003cp\u003eWe conduct five families of robustness checks, summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, to ensure that our findings are not artefacts of specific modelling choices.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness Check Summary\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCheck\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKey Result\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVerdict\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlternative Cholesky orderings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 alternative orderings: fiscal before monetary; reserves last; WUI endogenous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSCCI values and IRF signs unchanged in 11/12 quarterly comparisons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFindings robust to ordering\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlternative lag specifications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;1 and p\u0026thinsp;=\u0026thinsp;3 vs. benchmark p\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGDP response magnitudes within \u0026plusmn;\u0026thinsp;0.15pp; SCCI correlation r\u0026thinsp;\u0026gt;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRobust to lag choice\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlternative uncertainty measure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBaker-Bloom-Davis EPU Index replaces WUI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSCCI time profile nearly identical (r\u0026thinsp;=\u0026thinsp;0.88); IRF magnitudes scale proportionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRobust to uncertainty measure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubsample stability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-GFC (2000\u0026ndash;2007) vs. post-GFC (2008\u0026ndash;2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSCCI\u0026ndash;resilience relationship holds in both subsamples; threshold finding stronger post-GFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo structural break\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNARDL benchmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNonlinear ARDL (Shin et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) as static benchmark comparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNARDL confirms positive SCCI\u0026ndash;growth relationship; TVP-VAR captures regime variation NARDL cannot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTVP-VAR adds time-varying dimension\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eR1: Alternative Variable Orderings\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Cholesky decomposition implies that contemporaneously, higher-ordered variables do not affect lower-ordered ones within the same quarter. To test sensitivity to this assumption, we re-estimate the TVP-VAR under three alternative orderings: (a) government expenditure ordered before the policy rate (fiscal-first), (b) foreign reserves ordered last (buffer-as-residual), and (c) WUI ordered contemporaneously endogenous with domestic variables. In 11 of 12 crisis-episode/variable comparisons, the SCCI values are unchanged and the GDP IRF shapes are qualitatively identical. This finding consistent with the general TVP-VAR literature where Cholesky ordering sensitivity is limited when variables are not highly contemporaneously correlated (Chan, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) provides strong evidence that our results are not driven by the specific ordering chosen.\u003c/p\u003e \u003cp\u003e \u003cb\u003eR2 and R3: Lag and Uncertainty Measure Sensitivity\u003c/b\u003e \u003c/p\u003e \u003cp\u003eEstimating with p\u0026thinsp;=\u0026thinsp;1 and p\u0026thinsp;=\u0026thinsp;3 yields GDP response magnitudes within \u0026plusmn;\u0026thinsp;0.15 percentage points of the benchmark p\u0026thinsp;=\u0026thinsp;2 results, and SCCI time series with correlations exceeding 0.91 with the benchmark. Replacing the WUI with the Baker-Bloom-Davis Economic Policy Uncertainty (EPU) Index which Dai et al. (2019) show is highly consistent with the WUI globally produces an SCCI time series with correlation r\u0026thinsp;=\u0026thinsp;0.88 against the WUI-based SCCI, and IRF magnitudes that scale proportionally with the larger EPU values. These results confirm that neither the lag specification nor the choice of uncertainty proxy drives the findings.\u003c/p\u003e \u003cp\u003e \u003cb\u003eR4: Subsample Stability\u003c/b\u003e \u003c/p\u003e \u003cp\u003eEstimating separately over 2000Q1\u0026ndash;2007Q4 (pre-GFC) and 2008Q1\u0026ndash;2023Q4 (post-GFC) reveals that the positive SCCI\u0026ndash;resilience relationship holds in both subsamples. Notably, the threshold effect of reserves on coordination effectiveness is stronger in the post-GFC subsample consistent with the thesis that as Indonesia's institutional framework matured and reserves grew, the CCR mechanism became more potent. No significant structural break is detected using the Nyblom (1989) parameter stability test at standard significance levels.\u003c/p\u003e \u003cp\u003e \u003cb\u003eR5: NARDL Benchmark\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs a final check, we estimate a nonlinear ARDL model (Shin et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) using the SCCI as an explanatory variable for GDP growth, controlling for the WUI and lagged output. The positive SCCI coefficient (estimated at approximately\u0026thinsp;+\u0026thinsp;0.38, significant at 1%) in the NARDL confirms the coordination\u0026ndash;resilience relationship in a simpler framework. Crucially, the NARDL also confirms the asymmetry: positive SCCI shocks (coordination improving) have stronger GDP effects than negative SCCI shocks (coordination deteriorating). However, the NARDL cannot capture the time-varying nature of these effects the central contribution of the TVP-VAR demonstrating that the two approaches are complementary rather than competing.\u003c/p\u003e"},{"header":"7. Discussion","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e7.1 Theoretical Implications of CCR\u003c/h2\u003e \u003cp\u003eOur results provide the first empirical validation of the Coordination-Conditioned Resilience concept and carry several theoretical implications. First, they demonstrate that economic resilience in an open emerging market is not a stable structural property but a time-varying, policy-contingent outcome. This extends Martin's (2012) evolutionary framework which focuses on economic structure as the source of resilience by identifying the policy coordination regime as an additional, potentially more tractable determinant. Unlike industrial composition or labour market institutions, which change slowly, the policy coordination regime can shift rapidly, creating both vulnerability and opportunity.\u003c/p\u003e \u003cp\u003eSecond, our findings provide the first empirical bridge between the game-theoretic literature on fiscal-monetary interaction and the macroeconomic resilience literature. The correspondence between SCCI\u0026thinsp;=\u0026thinsp;+\u0026thinsp;1 and cooperative Pareto equilibrium, established through our Nash equilibrium validation exercise (following Salimi et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), demonstrates that the welfare-loss-minimizing cooperative solution is also the resilience-maximizing solution an equivalence not previously established in the literature.\u003c/p\u003e \u003cp\u003eThird, the threshold-conditioned amplification of coordination effectiveness by reserve adequacy provides new empirical content for the quadrilemma framework (Aizenman, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Our estimated threshold (approximately 10% of GDP) suggests a quantifiable reserve adequacy target that enables full monetary policy flexibility, a result with immediate policy relevance for emerging market reserve management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e7.2 Policy Implications\u003c/h2\u003e \u003cp\u003eFor Indonesian policymakers, the central message is that the institutional architecture for fiscal-monetary coordination is a direct determinant of macroeconomic resilience not merely a matter of procedural efficiency. The successful 2009 and 2020 responses demonstrate what is achievable when BI and Kemenkeu align their instruments in the cooperative direction. The 2013\u0026ndash;2015 and 2022\u0026ndash;2023 episodes demonstrate the costs of divergence under external pressure.\u003c/p\u003e \u003cp\u003eA concrete policy recommendation flows from the threshold finding: maintaining foreign reserves above approximately 10% of GDP is not just insurance against sudden stops but a direct enabler of monetary policy flexibility, which in turn enables more effective fiscal-monetary coordination. The fact that Indonesia's reserves grew from USD 28\u0026nbsp;billion in 2000 to USD 145\u0026nbsp;billion in 2024 from approximately 6% to 11% of GDP represents a structural improvement in coordination capacity, not just in crisis insurance.\u003c/p\u003e \u003cp\u003eFor other emerging market economies, the CCR framework provides a diagnostic tool: by computing the SCCI in real time, policymakers can monitor whether their fiscal-monetary mix is approximating the cooperative Pareto optimum or drifting toward Nash substitutability. Central banks and finance ministries in economies with managed float exchange rates and active reserve management including Thailand, Philippines, Vietnam, Peru, and Colombia face structurally similar trilemma constraints, and the CCR mechanism is likely operative in those contexts as well.\u003c/p\u003e \u003c/div\u003e"},{"header":"8. Conclusion","content":"\u003cp\u003eThis paper has introduced and empirically validated the concept of Coordination-Conditioned Resilience (CCR) the proposition that Indonesia's economic resilience to global uncertainty is a dynamic, policy-contingent outcome that depends on the degree of fiscal-monetary complementarity, conditioned by reserve adequacy and institutional credibility. Using a TVP-VAR model with stochastic volatility estimated over 2000Q1\u0026ndash;2023Q4 and a theoretically grounded Sign-Concordance Coordination Index (SCCI), we have documented three main findings corresponding to our three testable hypotheses.\u003c/p\u003e \u003cp\u003eH1 is supported: SCCI values are significantly positively associated with GDP recovery following WUI shocks, with estimated effects of approximately 0.4 percentage points of additional quarterly GDP recovery per unit of coordination in the high-reserve regime. H2 is supported: the coordination\u0026ndash;resilience relationship is non-linear and threshold-conditioned, with coordination effects approximately 2.5 times larger when foreign reserves exceed approximately 10% of GDP. H3 is supported: policy complementarity (SCCI\u0026thinsp;=\u0026thinsp;+\u0026thinsp;1) characterizes demand-shock-type episodes (GFC, COVID-19) while substitutability (SCCI \u0026rarr; \u0026minus;1) characterizes external monetary pressure episodes (taper tantrum, global tightening), consistent with the game-theoretic prediction that the nature of the shock determines the strategic equilibrium.\u003c/p\u003e \u003cp\u003eFive families of robustness checks confirm that these findings are not artefacts of specific modelling assumptions. The results are stable across alternative variable orderings, lag specifications, uncertainty measures, subsamples, and benchmark models.\u003c/p\u003e \u003cp\u003eThe CCR concept makes three contributions to the literature. It provides the first formal theoretical bridge between the macroeconomic policy coordination literature and the economic resilience literature. It establishes an empirically tractable coordination measure (SCCI) grounded in both the sign-restriction VAR tradition and game-theoretic models of fiscal-monetary interaction. And it provides the first evidence of a threshold-conditioned non-linearity in the coordination\u0026ndash;resilience relationship, with implications for reserve management policy.\u003c/p\u003e \u003cp\u003eSeveral limitations warrant acknowledgment. The TVP-VAR is a reduced-form framework that may not fully identify structural policy shocks in the presence of strong endogeneity. The Nash equilibrium validation is based on a stylized loss function; richer institutional modeling could improve this component. The SCCI captures coordination in response to uncertainty shocks only; a broader coordination measure accounting for domestic business cycle shocks would enrich the analysis. These limitations define a productive agenda for future research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003e7.2 Competing Interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003e7.1 Funding\u003c/h2\u003e \u003cp\u003eThe authors did not receive support from any organization for the submitted work.\u003c/p\u003e\u003ch2\u003e7.4 Authors\u0026rsquo; Contributions\u003c/h2\u003e \u003cp\u003eConceptualization, Methodology, Formal Analysis, and Writing \u0026ndash; Original Draft were performed by the first author. Writing \u0026ndash; Review \u0026amp; Editing and Supervision were performed by the second author.\u003c/p\u003e\u003ch2\u003e7.3 Data Availability\u003c/h2\u003e \u003cp\u003eThe data used in this study are publicly available. Real GDP growth, CPI inflation, Bank Indonesia\u0026rsquo;s policy rate, IDR/USD exchange rate, foreign exchange reserves, and real government expenditure data were obtained from the Federal Reserve Bank of St. Louis (FRED) at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fred.stlouisfed.org\u003c/span\u003e\u003cspan address=\"https://fred.stlouisfed.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The World Uncertainty Index (WUI) data are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://worlduncertaintyindex.com\u003c/span\u003e\u003cspan address=\"https://worlduncertaintyindex.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhir H, Bloom N, Furceri D (2022) The World Uncertainty Index. 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Economic Research-Ekonomska Istraživanja 32(1):1478\u0026ndash;1499. https://doi.org/10.1080/1331677X.2019.1669063 \u003c/li\u003e\n\u003cli\u003eTrippl M, Fastenrath S, Isaksen A (2024) Rethinking regional economic resilience: preconditions and processes shaping transformative resilience. European Urban and Regional Studies 31(2):101\u0026ndash;115. https://doi.org/10.1177/09697764231172326 \u003c/li\u003e\n\u003cli\u003eWorld Bank. (2025). World Development Indicators. https://data.worldbank.org \u003c/li\u003e\n\u003cli\u003eZhang E (2024) Coordination of monetary and fiscal policies from a game theoretic perspective. Advances in Economics, Management and Political Sciences 107:10\u0026ndash;14. https://doi.org/10.54254/2754-1169/107/2024GA0119 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Coordination-Conditioned Resilience, fiscal-monetary coordination, TVP-VAR, World Uncertainty Index, Indonesia, emerging markets, Sign-Concordance Index","lastPublishedDoi":"10.21203/rs.3.rs-9663421/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9663421/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper introduces the concept of Coordination-Conditioned Resilience (CCR) the proposition that the resilience of an emerging market economy to global uncertainty is not a fixed structural property, but a policy-contingent outcome that depends on the degree of fiscal-monetary complementarity, itself conditioned by reserve adequacy and institutional credibility. We test this concept empirically for Indonesia using a Time-Varying Parameter Vector Autoregression (TVP-VAR) model with stochastic volatility estimated on quarterly data spanning 2000Q1–2023Q4. A seven-variable system incorporates real GDP growth, CPI inflation, the Bank Indonesia policy rate, real government expenditure, the IDR/USD exchange rate, foreign reserves, and the World Uncertainty Index (WUI). Policy coordination is operationalized through a Sign-Concordance Coordination Index (SCCI) grounded in the sign-restricted VAR literature and game-theoretic models of fiscal-monetary interaction. Time-varying impulse response functions and forecast error variance decompositions are computed across three crisis regimes: the 2008 Global Financial Crisis, the 2020 COVID-19 shock, and the 2022–2023 global tightening cycle. Results show that fiscal-monetary complementarity as captured by the SCCI significantly amplifies output resilience, with each unit increase in coordination associated with approximately 0.4 percentage points of additional GDP recovery per quarter following an uncertainty shock. The relationship is non-linear: coordination effects are strongest when foreign reserve buffers exceed approximately 10% of GDP, suggesting a threshold-dependent mechanism. Extensive robustness checks confirm findings across alternative orderings, lag specifications, uncertainty measures, and subsamples. These results extend Martin's (2012) evolutionary resilience framework to the macroeconomic policy domain and provide new evidence that policy interactions, rather than individual instruments, are an important determinant of emerging market resilience.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification: \u003c/strong\u003eE52, E62, E63, F41, O53, C32\u003c/p\u003e","manuscriptTitle":"Macroeconomic Policy Coordination and Economic Resilience in Indonesia: A Time-Varying Analysis under Global Uncertainty","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 13:17:08","doi":"10.21203/rs.3.rs-9663421/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b2035050-4f3b-4f03-9831-ca54a04dd826","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":68000786,"name":"Econometrics"},{"id":68000787,"name":"Macroeconomics"}],"tags":[],"updatedAt":"2026-05-13T13:17:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 13:17:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9663421","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9663421","identity":"rs-9663421","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}