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

Author

Listed:
  • Kirsten Thompson
  • Reneé van Eyden
  • Rangan Gupta

Abstract

The importance of financial instability for the world economy has been severely demonstrated since the 2007–8 global financial crisis, highlighting the need for a better understanding of financial conditions. We consider a financial conditions index (FCI) for South Africa that is constructed from sixteen financial variables and test whether the FCI does better than its individual financial components in forecasting the key macroeconomic variables of output growth, inflation, and interest rates. Two sets of out-of-sample forecasts are obtained—one from a benchmark autoregressive (AR) model and one from a nested autoregressive distributed lag (ARDL) model that includes one financial variable at a time. This concept of forecast encompassing is used to examine the out-of-sample forecasting ability of these financial variables as well as of the FCI, while also controlling for data mining.

Suggested Citation

  • Kirsten Thompson & Reneé van Eyden & Rangan Gupta, 2015. "Testing the Out-of-Sample Forecasting Ability of a Financial Conditions Index for South Africa," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 51(3), pages 486-501, May.
    • Handle: RePEc:mes:emfitr:v:51:y:2015:i:3:p:486-501
    DOI: 10.1080/1540496X.2015.1025664
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    Cited by:

    1. Rangan Gupta & Hylton Hollander & Rudi Steinbach, 2020. "Forecasting output growth using a DSGE-based decomposition of the South African yield curve," Empirical Economics, Springer, vol. 58(1), pages 351-378, January.
    2. 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.
    3. Tomislav Globan, 2018. "Financial supply cycles in post-transition Europe – introducing a composite index for financial supply," Post-Communist Economies, Taylor & Francis Journals, vol. 30(4), pages 482-505, July.
    4. 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.
    5. Balcilar, Mehmet & Gupta, Rangan & van Eyden, Reneé & Thompson, Kirsten & Majumdar, Anandamayee, 2018. "Comparing the forecasting ability of financial conditions indices: The case of South Africa," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 245-259.
    6. repec:ipg:wpaper:2014-468 is not listed on IDEAS

    More about this item

    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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