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Testing the Out-of-Sample Forecasting Ability of a Financial Conditions Index for South Africa

Author

Listed:
  • Kirsten Thompson

    () (Department of Economics, University of Pretoria)

  • Renee van Eyden

    () (Department of Economics, University of Pretoria)

  • Rangan Gupta

    () (Department of Economics, University of Pretoria)

Abstract

The importance of financial instability for the world economy has been severely demonstrated since the 2007/08 global financial crisis, highlighting the need for a better understanding of financial conditions. We use a financial conditions index (FCI) for South Africa previously constructed from 16 financial variables to test whether the rolling-window estimated FCI does better than its individual financial components in forecasting key macroeconomic variables, such as output growth, inflation and interest rates. The concept of forecast encompassing is used to examine the forecasting ability of these variables controlling for data-mining. We find that the rolling-window estimated FCI has out-of-sample forecasting ability with respect to manufacturing output growth at the one, three and six month horizons, but has no forecasting ability with respect to inflation and interest rates.

Suggested Citation

  • Kirsten Thompson & Renee van Eyden & Rangan Gupta, 2013. "Testing the Out-of-Sample Forecasting Ability of a Financial Conditions Index for South Africa," Working Papers 201383, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201383
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    References listed on IDEAS

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    1. Kilian, Lutz, 1999. "Exchange Rates and Monetary Fundamentals: What Do We Learn from Long-Horizon Regressions?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 491-510, Sept.-Oct.
    2. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
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    4. Luc Bauwens & Gary Koop & Dimitris Korobilis & Jeroen V.K. Rombouts, 2015. "The Contribution of Structural Break Models to Forecasting Macroeconomic Series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 596-620, June.
    5. Gary Koop & Simon M. Potter, 2007. "Estimation and Forecasting in Models with Multiple Breaks," Review of Economic Studies, Oxford University Press, vol. 74(3), pages 763-789.
    6. Luc Bauwens & Gary Koop & Dimitris Korobilis & Jeroen V.K. Rombouts, 2015. "The Contribution of Structural Break Models to Forecasting Macroeconomic Series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 596-620, June.
    7. M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2006. "Forecasting Time Series Subject to Multiple Structural Breaks," Review of Economic Studies, Oxford University Press, vol. 73(4), pages 1057-1084.
    8. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    9. Kirsten Thompson & Renee van Eyden & Rangan Gupta, 2013. "Identifying a financial conditions index for South Africa," Working Papers 201333, University of Pretoria, Department of Economics.
    10. David E. Rapach & Christian E. Weber, 2004. "Financial Variables and the Simulated Out-of-Sample Forecastability of U.S. Output Growth Since 1985: An Encompassing Approach," Economic Inquiry, Western Economic Association International, vol. 42(4), pages 717-738, October.
    11. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
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    13. repec:edn:sirdps:274 is not listed on IDEAS
    14. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
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    Cited by:

    1. Balcilar, Mehmet & Thompson, Kirsten & Gupta, Rangan & van Eyden, Reneé, 2016. "Testing the asymmetric effects of financial conditions in South Africa: A nonlinear vector autoregression approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 43(C), pages 30-43.
    2. Melo, Luis F. & Loaiza, Rubén A. & Villamizar-Villegas, Mauricio, 2016. "Bayesian combination for inflation forecasts: The effects of a prior based on central banks’ estimates," Economic Systems, Elsevier, vol. 40(3), pages 387-397.
    3. repec:ipg:wpaper:2014-468 is not listed on IDEAS

    More about this item

    Keywords

    Financial conditions index; forecast encompassing; data-mining; financial crisis;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G01 - Financial Economics - - General - - - Financial Crises

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