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Forecasting monetary policy rules in South Africa

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  • Naraidoo, Ruthira
  • Paya, Ivan

Abstract

This paper is the first one to: (i) provide in-sample estimates of linear and nonlinear Taylor rules, augmented with an indicator of financial stability, for the case of South Africa, and (ii) analyse the ability of linear and nonlinear monetary policy rule specifications, as well as nonparametric and semiparametric models, to forecast the nominal interest rate setting that describes the South African Reserve Bank’s (SARB) policy decisions. Our results indicate, first, that asset prices are taken into account when setting interest rates; second, that there are nonlinearities in the monetary policy rule; and third, that forecasts constructed from semiparametric models perform particularly well over the inflation targeting regime and that there are gains from semiparametric models in forecasting the interest rates as the forecasting horizon lengthens.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 28 (2012)
Issue (Month): 2 ()
Pages: 446-455

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Handle: RePEc:eee:intfor:v:28:y:2012:i:2:p:446-455

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Web page: http://www.elsevier.com/locate/ijforecast

Related research

Keywords: Taylor rules; Nonlinearity; Nonparametric; Semiparametric; Forecasting;

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References

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Citations

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Cited by:
  1. Ruthira Naraidoo & Ivan Paya, 2010. "Forecasting Monetary Policy Rules in South Africa," Working Papers 189, Economic Research Southern Africa.
  2. 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.
  3. Ellyne, Mark & Veller, Carl, 2011. "What is the SARB's inflation targeting policy, and is it appropriate?," MPRA Paper 42134, University Library of Munich, Germany.
  4. Baaziz, Yosra & Labidi, Moez & Lahiani, Amine, 2013. "Does the South African Reserve Bank follow a nonlinear interest rate reaction function?," Economic Modelling, Elsevier, vol. 35(C), pages 272-282.

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