Forecasting the South African labour market indicators: A comparison of ARIMA, count series models and machine learning regressors

Abstract This paper compared count series, time series and machine learning models to determine the best data type (between count data and continuous data) and the best model for forecasting the labour market variables. Data from Statistics South Africa’s Quarterly Labour Force Survey (2008–2021) was used to compare ARIMA, Poisson autoregressive (PAR), negative binomial regression (NBR), generalised Poisson regression (GPR), support vector regression (SVR) and the multilayer perceptron (MLP) regressor based on the MAE, RMSE, MAPE, MSE and the plots of the actual values versus the forecasts from the models. The study showed that the most accurate approach to estimating the future values of the labour market variables is to implement PAR to forecast the counts of people who are unemployed, employed, and non-economically active, and then use these forecasts to compute the future values of unemployment rate, absorption rate and labour force participation rate.