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Comparing the Forecasting Ability of Financial Conditions Indices: The Case of South Africa

Listed author(s):
  • Mehmet Balcilar

    ()

    (Department of Economics, Eastern Mediterranean University)

  • Rangan Gupta

    ()

    (Department of Economics, University of Pretoria)

  • Renee van Eyden

    ()

    (Department of Economics, University of Pretoria)

  • Kirsten Thompson

    ()

    (Department of Economics, University of Pretoria)

In this paper we test the forecasting ability of three estimated financial conditions indices (FCIs) with respect to key macroeconomic variables of output growth, inflation and interest rates. We do this by forecasting the aforementioned macroeconomic variables based on the information contained in the three alternative FCIs using a Bayesian VAR (BVAR), nonlinear logistic vector smooth transition autoregression (VSTAR) and nonparametric (NP) and semi-parametric (SP) regressions, and compare the results with the standard benchmarks of random-walk, univariate autoregressive and classical VAR models. The three FCIs are constructed using rolling-window principal component analysis (PCA), dynamic model averaging (DMA) in the context of a time-varying parameter factor-augmented vector autoregressive (TVP-FAVAR) model, and a time-varying parameter vector autoregressive (TVP-VAR) model with constant factor loadings. Our results suggest that the VSTAR model performs best in the case of forecasting manufacturing production and inflation, while a SP specification proves to be the best for forecasting the interest rate. More importantly, statistics testing for significant differences in forecast errors across models corroborate the finding of superior predictive ability of the nonlinear models.

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File URL: http://repec.economics.emu.edu.tr/RePEc/emu/wpaper/15-06.pdf
File Function: First version, 2015
Download Restriction: no

Paper provided by Eastern Mediterranean University, Department of Economics in its series Working Papers with number 15-06.

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Length: 28 pages
Date of creation: 2015
Handle: RePEc:emu:wpaper:15-06.pdf
Contact details of provider: Phone: 90 (392) 630-1291
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Web page: http://economics.emu.edu.tr/

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  1. Piergiorgio Alessandri & Haroon Mumtaz, 2017. "Financial conditions and density forecasts for US output and inflation," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 24, pages 66-78, March.
  2. repec:ipg:wpaper:2014-468 is not listed on IDEAS
  3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
  4. Koop, Gary & Korobilis, Dimitris, 2014. "A new index of financial conditions," European Economic Review, Elsevier, vol. 71(C), pages 101-116.
  5. Balcilar, Mehmet & Thompson, Kirsten & Gupta, Rangan & van Eyden, ReneƩ, 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.
  6. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
  7. Estrella, Arturo & Hardouvelis, Gikas A, 1991. " The Term Structure as a Predictor of Real Economic Activity," Journal of Finance, American Finance Association, vol. 46(2), pages 555-576, June.
  8. Jan Hatzius & Peter Hooper & Frederic S. Mishkin & Kermit L. Schoenholtz & Mark W. Watson, 2010. "Financial Conditions Indexes: A Fresh Look after the Financial Crisis," NBER Working Papers 16150, National Bureau of Economic Research, Inc.
  9. Arora Siddharth & Little Max A. & McSharry Patrick E., 2013. "Nonlinear and nonparametric modeling approaches for probabilistic forecasting of the US gross national product," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 395-420, September.
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