Structural minerals fluctuations and the macroeconomy

Raputsoane, Leroi (2025): Structural minerals fluctuations and the macroeconomy.

Abstract

This paper analyses the structural relationship between minerals fluctuations and the macroeconomy in South Africa. This is achieved by isolating the trend component of output of the aggregate minerals industry, together with output of disaggregated minerals and comparing their fluctuations with the trend component of aggregate, or economy wide, output. The results have shown a statistically significant, and predominantly positive, relationship between aggregate, or economy wide, output and output of Mining, at structural, or long term, periodicities. The results have further shown a positive, or procyclical, relationship between aggregate, or economy wide, output and output of Chromium, Manganese and Quarrying, an acyclical relationship between aggregate output and output of Nickel and Other metals, while they show a negative, or countercyclical, relationship between aggregate output and output of Coal, Iron ore, Copper, PGMs, Gold, Diamonds and Other non metals. The paper recommends a comprehensive determination of the temporal relationship between the minerals industry and macroeconomic indicators to inform targeted policy decision making, where appropriate.

• Item Type: MPRA Paper
• Language: English
• Keywords: Minerals fluctuations, Minerals industry, Economic cycles
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General; D - Microeconomics > D2 - Production and Organizations > D20 - General; E - Macroeconomics and Monetary Economics > E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy > E20 - General; L - Industrial Organization > L7 - Industry Studies: Primary Products and Construction > L72 - Mining, Extraction, and Refining: Other Nonrenewable Resources

References

Bartels, L. M. (1997). Specification Uncertainty and Model Averaging. American Journal of Political Science, 41(2):641–674.

Baxter, M. and King, R. G. (1999). Measuring Business Cycles: Approximate Band Pass Filters for Economic Time Series. Review of Economics and Statistics, 81(4):575–593.

Beber, A., Brandt, M. W., and Kavajecz, K. A. (2011). What Does Equity Sector Orderflow Tell us About the Economy? Review of Financial Studies, 24(11):3688–3730.

Blanchard, O. J., Hall, R. E., and Hubbard, R. G. (1986). Market Structure and Macroeconomic Fluctuations. Brookings Papers on Economic Activity, 1986(2):285–338.

Blanchard, O. J. and Quah, D. (1988). The Dynamic Effects of Aggregate Demand and Supply Disturbances. Working Papers Series, 2737. National Bureau of Economic Research (NBER).

Burns, A. F. and Mitchell, W. C. (1946). Measuring Business Cycles. National Bureau of Economic Research (NBER) Books, Cambridge, Massachusetts.

Campbell, J. Y. and Mankiw, N. G. (1987a). Are Output Fluctuations Transitory? The Quarterly Journal of Economics, 102(4):857–880.

Campbell, J. Y. and Mankiw, N. G. (1987b). Permanent and Transitory Components in Macroeconomic Fluctuations. American Economic Review, 77(2):111–117.

Campos, N. F., De Grauwe, P., and Ji, Y. (2025). Structural Reforms and Economic Performance: The Experience of Advanced Economies. Journal of Economic Literature, 63(1):111–63.

Christiano, L. J., Eichenbaum, M., and Evans, C. L. (2005). Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy. Journal of Political Economy, 113(1):1–45.

Christiano, L. J. and Fitzgerald, T. J. (2003). The Band Pass Filter. International Economic Review, 44(2):435–465.

Corden, W. M. (1980). Relationships Between Macroeconomic and Industrial Policies. The World Economy, 3(2):167–184.

Diebold, F. X. and Rudebusch, G. D. (1970). Measuring Business Cycles: A Modern Perspective. Review of Economics and Statistics, 78(1):67–77.

European Central Bank (ECB) (2012). Stock Prices and Economic Growth. Monthly Bulletin, October. European Central Bank (ECB).

Feldkircher, M. and Zeugner, S. (2015). Bayesian Model Averaging Employing Fixed and Flexible Priors. Journal of Statistical Software, 68(4):1–37.

Friedman, M., Schwartz, A. J., et al. (1963). Money and Business Cycles. Bobbs-Merrill Company, Indianapolis, Indiana.

Gali, J. (1992). How Well Does the IS-LM Model Fit Post War US Data? The Quarterly Journal of Economics, 107(2):709–738.

Gordon, R. J. (2007). The American Business Cycle: Continuity and Change. University of Chicago Press, Chicago.

Hinne, M., Gronau, Q. F., van den Bergh, D., and Wagenmakers, E.-J. (2020). A Conceptual Introduction to Bayesian Model Averaging. Advances in Methods and Practices in Psychological Science, 3(2):200–215.

Hodrick, R. and Prescott, E. C. (1997). Postwar U.S. Business Cycles: An Empirical Investigation. Journal of Money, Credit and Banking, 29(1):1–16.

Hoeting, J. A., Madigan, D., Raftery, A. E., and Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 44(4):382–401.

Kydland, F. E. and Prescott, E. C. (1990). Business Cycles: Real Facts and a Monetary Myth. Quarterly Review, 4:3–18. Federal Reserve Bank of Minneapolis.

Leamer, E. E. (1978). Specification Searches: Ad Hoc Inference With Non Experimental Data. John Wiley and Sons Inc., New Jersey.

Mise, E., Kimand, T., and Newbold, P. (2005). On Suboptimality of the Hodrick Prescott Filter at Time Series Endpoints. Journal of Macroeconomics, 27(1):53–67.

Morgan Stanley Capital International (MSCI) (2014). Cyclical and Defensive Sectors. Indexes Methodology, June. Morgan Stanley Capital International (MSCI).

Nelson, C. R. and Plosser, C. R. (1982). Trends and Random Walks in Macroeconomic Time Series: Some Evidence and Implications. Journal of Monetary Economics, 10(2):139–162.

Nelson, E. (2005). Monetary Policy Neglect and the Great Inflation in Canada, Australia, and New Zealand. International Journal of Central Banking, 1(1):133–179.

Ostry, J. D., Prati, A., and Spilimbergo, A. (2009). III Structural Reforms: Measurement and Trends. In Ostry, J. D., Prati, A., and Spilimbergo, A., editors, Structural Reforms and Economic Performance in Advanced and Developing Countries, pages 4–7. International Monetary Fund (IMF).

Petersen, B. and Strongin, S. (1996). Why are Some Industries more Cyclical than Others? Journal of Business and Economic Statistics, 14(2):189–198.

Quah, D. (1988). Sources of Business Cycle Fluctuations: Comments. Macroeconomics Annual, 3:151–155. National Bureau of Economic Research (NBER).

Ravn, M. O. and Uhlig, H. (2002). On Adjusting the Hodrick-Prescott Filter for the Frequency of Observations. Review of Economics and Statistics, 84(2):371–376.

Romer, C. D. (1986). Is the Stabilization of the Postwar Economy a Figment of the Data? The American Economic Review, 76(3):314–334.

Romer, C. D. (1993). Business Cycles. In Henderson, D. R., editor, The Fortune: Encyclopedia of Economics, volume 330.03 F745f. Warner Books.

Shapiro, M. D. and Watson, M. W. (1988). Sources of Business Cycle Fluctuations. In Fischer, S., editor, Macroeconomics Annual, volume 3, pages 111–156. National Bureau of Economic Research (NBER), Cambridge, Massachusetts.

Stock, J. H. and Watson, M. W. (1999). Business Cycle Fluctuations in US Macroeconomic Time Series. Handbook of Macroeconomics, 1(Part A):3–64.

Varian, H. R. (2014). Big Data: New Tricks for Econometrics. Journal of Economic Perspectives, 28(2):3–28.

Zellner, A. (1986). On Assessing Prior Distributions and Bayesian Regression Analysis With g Prior Distributions. In Goel, P. and Zellner, A., editors, Bayesian Inference and Decision Techniques, Volume 6, pages 233–243. Elsevier Science, Amsterdam, Netherlands.