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Forecasting Key Macroeconomic Variables of the South African Economy Using Bayesian Variable Selection

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Author Info

  • Mirriam Chitalu Chama-Chiliba

    (Department of Economics, University of Pretoria)

  • Rangan Gupta

    ()
    (Department of Economics, University of Pretoria)

  • Nonophile Nkambule

    (Department of Economics, University of Pretoria)

  • Naomi Tlotlego

    ()
    (Department of Economics, University of Pretoria)

Abstract

We compare the forecasting performances of the classical and the Minnesota-type Bayesian vector autoregressive (VAR) models with those of linear (fixed-parameter) and nonlinear (time-varying parameter) VARs involving a stochastic search algorithm for variable selection, estimated using Markov Chain Monte Carlo methods. In this regard, we analyze the forecasting performances of all these models in predicting one- to eight-quarters-ahead of the growth rate of GDP, the consumer price index inflation rate and the three months Treasury bill rate for South Africa over an out-of-sample period of 2000:Q1-2011:Q2, using an in-sample period of 1960:Q1-1999:Q4. In general, we find that variable selection, whether imposed on a time-varying VAR or a fixed parameter VAR, and non-linearity in VARs play an important part in improving predictions when compared to the linear fixed coefficients classical VAR. However, we do not observe marked gains in forecasting power across the different Bayesian models, as well as, over the classical VAR model, possibly because the problem of over parameterization in the classical VAR is not that acute in our three-variable system.

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Bibliographic Info

Paper provided by University of Pretoria, Department of Economics in its series Working Papers with number 201132.

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Length: 11 pages
Date of creation: Dec 2011
Date of revision:
Handle: RePEc:pre:wpaper:201132

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Keywords: Forecasting; time varying parameters; variable selection; Bayesian vector autoregression;

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  1. Das, Sonali & Gupta, Rangan & Kabundi, Alain, 2009. "Could we have predicted the recent downturn in the South African housing market?," Journal of Housing Economics, Elsevier, vol. 18(4), pages 325-335, December.
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  5. Rangan Gupta & Alain Kabundi, 2008. "A Dynamic Factor Model for Forecasting Macroeconomic Variables in South Africa," Working Papers 200815, University of Pretoria, Department of Economics.
  6. Koop, Gary & Korobilis, Dimitris, 2009. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," MPRA Paper 20125, University Library of Munich, Germany.
  7. Rangan Gupta & Alain Kabundi, 2009. "Forecasting Macroeconomic Variables Using Large Datasets: Dynamic Factor Model versus Large-Scale BVARs," Working Papers 143, Economic Research Southern Africa.
  8. Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
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  10. Guangling 'Dave' Liu & Rangan Gupta & Eric Schaling, 2009. "A New-Keynesian DSGE model for forecasting the South African economy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(5), pages 387-404.
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  13. Spencer, David E., 1993. "Developing a Bayesian vector autoregression forecasting model," International Journal of Forecasting, Elsevier, vol. 9(3), pages 407-421, November.
  14. Rangan Gupta & Sonali Das, 2008. "Predicting Downturns in the US Housing Market: A Bayesian Approach," Working Papers 200821, University of Pretoria, Department of Economics.
  15. Rangan Gupta & Sonali Das, 2008. "Spatial Bayesian Methods of Forecasting House Prices in Six Metropolitan Areas of South Africa," Working Papers 200813, University of Pretoria, Department of Economics.
  16. Rangan Gupta & Alain Kabundi, 2008. "Forecasting Macroeconomic Variables in a Small Open Economy: A Comparison between Small- and Large-Scale Models," Working Papers 200830, University of Pretoria, Department of Economics.
  17. KOROBILIS, Dimitris, 2011. "VAR forecasting using Bayesian variable selection," CORE Discussion Papers 2011022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  18. Andersson, Michael K & Karlsson, Sune, 2007. "Bayesian Forecast Combination for VAR Models," Working Papers 2007:13, Örebro University, School of Business.
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  21. Balcilar, Mehmet & Gupta, Rangan & Shah, Zahra B., 2011. "An in-sample and out-of-sample empirical investigation of the nonlinearity in house prices of South Africa," Economic Modelling, Elsevier, vol. 28(3), pages 891-899, May.
  22. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
  23. Guangling (Dave) Liu & Rangan Gupta, 2006. "A Small-Scale DSGE Model for Forecasting the South African Economy," Working Papers 200621, University of Pretoria, Department of Economics.
  24. Rangan Gupta & Rudi Steinbach, 2010. "Forecasting Key Macroeconomic Variables of the South African Economy: A Small Open Economy New Keynesian DSGE-VAR Model," Working Papers 201019, University of Pretoria, Department of Economics.
  25. Sonali Das & Rangan Gupta & Alain Kabundi, 2011. "Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(2), pages 288-302, March.
  26. Rangan Gupta, 2007. "FORECASTING THE SOUTH AFRICAN ECONOMY WITH GIBBS SAMPLED BVECMs," South African Journal of Economics, Economic Society of South Africa, vol. 75(4), pages 631-643, December.
  27. George, Edward I. & Sun, Dongchu & Ni, Shawn, 2008. "Bayesian stochastic search for VAR model restrictions," Journal of Econometrics, Elsevier, vol. 142(1), pages 553-580, January.
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  29. Guangling “Dave” Liu & Rangan Gupta & Eric Schaling, 2010. "Forecasting the South African economy: a hybrid-DSGE approach," Journal of Economic Studies, Emerald Group Publishing, vol. 37(2), pages 181-195, May.
  30. Rangan Gupta, 2009. "Bayesian Methods Of Forecasting Inventory Investment," South African Journal of Economics, Economic Society of South Africa, vol. 77(1), pages 113-126, 03.
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