An assessment of the value of PMI and manufacturing sector growth in predicting overall economic output (GDP) in South Africa

Abstract

Macroeconomic indexes are useful tools in forecasting long and short-run changes in the economy. The purpose of this study is to assess the usefulness of the Purchasing Managers' Index (PMI), and changes in the manufacturing sector as predictors of economic output. This study is quantitative in nature and employed an ARDL econometric model, vector error correction (VEC) and Granger causality approaches to determine the short and long-run relationships amongst the variables. The ARDL method was used as the variables had a mixture of stationarity at levels I(0) and first difference I(1). The model used economic output measured as GDP, as the dependent variable, while PMI, output in the manufacturing sector and CPI (used as the control variable) were the independent variables. Quarterly data sets were obtained from Statistics South Africa and the Bureau of Economic Research (BER) for the period 2000 to 2017. Findings of the ARDL estimation revealed that the variables cointegrate in the long run and changes in manufacturing output had the highest impact on long-run economic growth of the three variables. In the short run, all independent variables had a significant impact on economic growth. The main findings from the Granger causality tests indicate that bi-directional causality exists between both PMI and GDP as well as between PMI and manufacturing output. Additionally, bi-directional causality was found between GDP and manufacturing, while CPI just causes manufacturing changes. The implications of the research is the confirmation of the importance of PMI, CPI and output of the manufacturing sector as indicators for changes in overall economic activity on a macro level.