AN ASSESSMENT OF THE VALUE OF PMI AND MANUFACTURING SECTOR GROWTH IN PREDICTING OVERALL ECONOMIC OUTPUT (GDP) IN SOUTH AFRICA

Yıl 2019, , 191 - 206, 01.12.2019

Daniel Francois Meyer Thomas Habanabakize

https://doi.org/10.34111/ijebeg.20191127

Cited By: 2

Öz

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.

Anahtar Kelimeler

ARDL, Economic output, MPI, South Africa

Kaynakça

• Altissimo, F., Cristadoro, R., Forni, M., Lippi, M. & Veronese, G. (2010). New Eurocoin: Tracking economic growth in real time. The Review of Economics and Statistics, 92(4), 1024-1034.
• Aprigliano, V. (2011). The relationship between the PMI and the Italian index of industrial production and the impact of the latest economic crisis. http://docplayer.net/38084361-Temi-di-discussione-the-relationship-between-the-pmi-and-the-italian-index-of-industrial-production-and-the-impact-of-the-latest-economic-crisis.html. Accessed 2018/06/15.
• Banerjee, A., Dolado, J. & Mestre, R. (1998). Error-correction mechanism tests for cointegration in a single equation framework. Journal of Time Series Analysis, 19(3), 267–283.
• Barnes, R. (2017). Economic indicators: Purchasing managers index (PMI). https://www.coursehero.com/file/5894246/EconomicIndicators/. Accessed 2018/06/14.
• Bayer, C. & Hanck, C. (2013). Combining non-cointegration tests. Journal of Time Series Analysis, 34(1), 83–95.
• BER. (2015). PMI: A monthly index of business conditions in the manufacturing sector. https://10513086/Downloads/BER%20pmi_pdf_absa.pdf. Accessed 2018/06/12.
• Boswijk, H.P. (1994). Testing for an unstable root in conditional and structural error correction models. Journal of Econometrics, 63(1), 37-60.
• Chien, Y. & Morris, P. (2016). PMI and GDP: Do they correlate for the united States? For China? Economic Synopses, Issue 6, pp. 1-2, 2016. SSRN: https://ssrn.com/abstract=2756951. Accessed 2018/06/05.
• Chin, H. (2017). PMI Report on China manufacturing. https://www.funggroup.com/eng/knowledge/research/PMI_november13.pdf. Accessed 2018/06/04.
• Engle, R.F. & Granger, C.W. (1987). Co-integration and error correction: Representation, estimation and testing. Econometrica: Journal of the Econometric Society, 55(2), 251–276.
• Harris, E.S. (1991). Tracking the economy with the purchasing manager’s index. Washington, DC. Federal Reserve Bank.
• Haug, A. (2002). Temporal aggregation and the power of cointegration tests: A Monte Carlo study. Oxford Bulletin of Economics and Statistics, 64, 399–412.
• Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive model. Econometrica: Journal of the Econometric Society, 59(6), 1551–1580.
• Joseph, A., Larrain, M. & Turnerc, C. (2011). Forecasting purchasing managers’ index with compressed interest rates and past values. Procedia Computer Science, 6, 213-218.
• IHS Markit. (2017). Interpreting PMI data. https://www.markiteconomics.com/survey/pdf.mvc/en_pmirecruitment. Accessed 2018/06/08.
• Khundrakpam, J.K. & George, A.T. (2013). An empirical analysis of the relationship between WPI and PMI-manufacturing price indices in India. https://ideas.repec.org/p/pra/mprapa/50929.html. Accessed 2018/06/07.
• Koenig, E.F. (2002). Using the purchasing managers’ index to assess the economy’s strength and the likely direction of monetary policy. Federal Reserve Bank of Dallas, Economic and Financial Policy Review, 1(6), 1-14.
• Lahiri, K. & Monokroussos, G. (2013). Nowcasting US GDP: The role of ISM business surveys. International Journal of Forecasting, 29(4), 644-658.
• Laurenceson, J. & Chai, J.C.H. (2003). Financial reform and economic development in China. Cheltenham, UK: Edward Elgar.
• Lindsey, M.D. & Pavur, R. (2005), As the PMI turns: A tool for supply chain managers. The Journal of Supply Chain Management, 41(3), 30-39.
• Narayan, P.K. (2005). The saving and investment nexus for China: Evidence from cointegration tests. Applied Economics, 37, 1979–1990. Pelaez, R.F. (2003). A reassessment of the purchasing managers' index. Business Economics, 38(4), 35-42.
• Pesaran, M.H., Shin, Y. & Smith, R.J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326.
• Phillips, P.C. & Ouliaris, S. (1990). Asymptotic properties of residual-based tests for cointegration. Econometrica: Journal of Econometric Society, 58(1), 165–193.
• Tsuchiya, Y. (2012). Is the purchasing managers’ index useful for assessing the economy’s strength? A directional analysis. Economics Bulletin, 32(2), 1302-1311.