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Forecasting South African macroeconomic variables with a Markov-switching small open-economy dynamic stochastic general equilibrium model

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Abstract

This paper seeks to identify evidence of regime-switching behaviour in the monetary policy response function and the variance of the shocks. It makes use of various specifications of a small open-economy Markov-switching dynamic stochastic general equilibrium model that is applied to South African data from 1989 to 2014. While the in-sample statistics suggest that some of the regime-switching models may provide superior results, the out-of-sample statistics suggest that the inclusion of various forms of regime-switching does not significantly improve upon the forecasting performance of the model. The results also suggest that the central bank response function has been consistently applied over the sample period.

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Notes

  1. Over the sample period the respective leaders of central bank include: Stals [1989], Mboweni [1999], Marcus [2009] and Kganyago [2014]; where the date of appointment is included in brackets.

  2. These crises would include the emerging market crises that originated in Mexico [1994], Asia [1997], Russia [1998] and Argentina [1999]. As South Africa has a relatively liquid financial market, the dot.com and other asset pricing bubbles may have also influenced the variance of economic shocks at particular points in time. Then lastly, the recent global financial crisis and the period of quantitative easing in developed world economies may have affected both the monetary policy response function and the variance of the shocks.

  3. These studies extend the work of Clarida et al. (2000) and Lubik and Schorfheide (2004), by considering the application of Markov-switching models for the identification different monetary policy regimes. Computational details that describe a robust method for the calculation of the posterior density for the complex likelihood function are contained in Sims and Zha (2004) and Sims et al. (2008).

  4. While these findings are of significant interest, the use of reduced-form models for monetary policy investigations have been criticised by Lucas (1976) for not incorporating forward-looking behaviour, while Galí (2008) and Christiano et al. (2011) note that reduced-form models have been largely unable to describe some of the essential features of monetary policy.

  5. To the best of our knowledge, this is the first paper that considers the use of a Markov-switching DSGE model that has been applied to South African data.

  6. Stock and Watson (2003), Primiceri and Justiniano (2008) and Fernández-Villaverde et al. (2010) also make note of importance of allowing for changes in the variances of shocks.

  7. See, Alpanda et al. (2010a, (2010b) for further details of the derivation of the model.

  8. While the distinction between the two regimes is controlled by the size of \(\varrho _{\kappa ,y}\), we see that the more significant difference in the central bank response function (when comparing the two regimes) relates to the reaction to a change in the rate of inflation.

  9. Similar notation is used for the variance in the other stochastic shocks and the regime with the larger variance in the shocks is denoted \(\vartheta = 1\).

  10. Alpanda et al. (2010a) provide a motivation for the inclusion of a risk-premium shock, when modelling South African macroeconomic data.

  11. Additional results from each of these models may be found in the online technical appendix.

  12. The online technical appendix includes further details regarding the solution and estimation techniques.

  13. This solution algorithm is implemented with the aid of the RISE toolbox that has been developed by Maih (2014).

  14. Additional results are also included in the online appendix.

  15. Hence, if the sample period started prior to this structural break the Markov-switching model would possibly only pick up on this behaviour and leave the remaining sample as one that is characterised as a single regime.

  16. To create a single measure of consumer price inflation, we combine the respective measures that existed prior to 2008 with that which was established under the current methodology, using the monthly weighting procedure that is discussed in Du Plessis et al. (2015).

  17. Christiano et al. (2011) provide details regarding the computation of this statistic.

  18. All of the parameter mode and standard deviations for each of the models have been included in the online appendix, where we also include figures for distributions of parameters (and their means) in the three models that provide superior in-sample statistics.

  19. The smoothed transition probabilities for all the other models are included in the online appendix.

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Authors and Affiliations

  1. Department of Economics, Eastern Mediterranean University, via Mersin 10, Famagusta, Turkish Republic of Northern Cyprus, Turkey

    Mehmet Balcilar

  2. IPAG Business School, Paris, France

    Mehmet Balcilar & Rangan Gupta

  3. Department of Economics, University of Pretoria, Pretoria, 0002, South Africa

    Mehmet Balcilar & Rangan Gupta

  4. School of Economics, Faculty of Commerce, University of Cape Town, Middle Campus, Private Bag, Rondebosch, 7701, South Africa

    Kevin Kotzé

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  1. Mehmet Balcilar
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  2. Rangan Gupta
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  3. Kevin Kotzé
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Correspondence to Rangan Gupta.

Additional information

The authors would like to thank the anonymous reviewer who provided generous and insightful comments. The remaining errors are those of the authors.

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Balcilar, M., Gupta, R. & Kotzé, K. Forecasting South African macroeconomic variables with a Markov-switching small open-economy dynamic stochastic general equilibrium model. Empir Econ 53, 117–135 (2017). https://doi.org/10.1007/s00181-016-1157-6

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  • DOI: https://doi.org/10.1007/s00181-016-1157-6

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