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Evaluating the forecasting performance of linear and nonlinear monetary policy rules for South Africa

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  • Kasai, Ndahiriwe
  • Naraidoo, Ruthira

Abstract

This paper compares forecast performance of linear and nonlinear monetary policy rules using South African data. Recursive forecasts values are computed for 1- to 12-steps ahead for the out-of-sample period 2006:01 to 2010:12. For the nonlinear models we use bootstrap method for multi-step ahead forecasts as opposed to point forecasts approach used for linear models. The aim of the paper is to evaluate the performance of three competing models in an out-of-sample forecasting exercise. Overall ranking reveals the superiority of the nonlinear model that distinguishes between downward and upward movements in the business cycles in closely matching the historical record. As such, forecasting performance tests reveal that the South African Reserve bank pays particular attention to business cycles movements when setting its policy rate.

Suggested Citation

  • Kasai, Ndahiriwe & Naraidoo, Ruthira, 2011. "Evaluating the forecasting performance of linear and nonlinear monetary policy rules for South Africa," MPRA Paper 40699, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:40699
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    References listed on IDEAS

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    Cited by:

    1. Mehmet Balcilar & Rangan Gupta & Kevin Kotzé, 2017. "Forecasting South African macroeconomic variables with a Markov-switching small open-economy dynamic stochastic general equilibrium model," Empirical Economics, Springer, vol. 53(1), pages 117-135, August.

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    More about this item

    Keywords

    Monetary policy rules; forecast evaluation;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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