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Forecasting South African Macroeconomic Data with a Nonlinear DSGE Model

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Author Info

  • Mehmet Balcilar

    ()
    (Department of Economics, Eastern Mediterranean University, Famagusta, North Cyprus,via Mersin 10, Turkey)

  • Rangan Gupta

    ()
    (Department of Economics, University of Pretoria)

  • Kevin Kotze

    ()
    (The School of Economics, Faculty of Commerce, University of Cape Town)

Abstract

This paper considers the forecasting performance of a nonlinear dynamic stochastic general equilibrium (DSGE) model. The results are compared to a wide selection of competing models, which include a linear DSGE model and a variety of vector autoregressive (VAR) models. The parameters in the VAR models are estimated with classical and Bayesian techniques; where some of the Bayesian models are augmented with stochastic-variable-selection, time-varying parameters, endogenous structural breaks and various forms of prior-shrinkage (which includes the Minnesota prior as well). The structure of the DSGE models follows that of New-Keynesian varieties, which allow for several nominal and real rigidities. The nonlinear DSGE model makes use of the second-order solution method of Schmitt-Grohe and Uribe (2004) and a particle filter to generate values for the unobserved variables. Most of the parameters in the models are estimated using maximum likelihood techniques. The models are applied to South African macroeconomic data, with an initial in-sample period of 1960Q1 to 1999Q4. The models are then estimated recursively, by extending the in-sample period by a quarter, to generate successive forecasts over the out-of-sample period, 2000Q1 to 2011Q4. We find that the forecasting performance of the nonlinear DSGE model is almost always significantly superior to that of it's linear counterpart; particularly over longer forecasting horizons. The nonlinear DSGE model also outperforms the selection of VAR models in most cases.

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Bibliographic Info

Paper provided by University of Pretoria, Department of Economics in its series Working Papers with number 201313.

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Length: 16 pages
Date of creation: Mar 2013
Date of revision:
Handle: RePEc:pre:wpaper:201313

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Keywords: Macroeconomic Forecasting; Linear and Nonlinear New-Keynesian DSGE; Vector Autoregressions; Bayesian Methods;

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References

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Citations

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Cited by:
  1. Sergey Ivashchenko, 2014. "Forecasting In a Non-Linear DSGE Model," EUSP Deparment of Economics Working Paper Series, European University at St. Petersburg, Department of Economics Ec-02/14, European University at St. Petersburg, Department of Economics.
  2. Annari de Waal & Renee van Eyden & Rangan Gupta, 2013. "Do we need a global VAR model to forecast inflation and output in South Africa?," Working Papers, University of Pretoria, Department of Economics 201346, University of Pretoria, Department of Economics.
  3. Rangan Gupta & Patrick T. kanda & Mampho P. Modise & Alessia Paccagnini, 2013. "DSGE Model-Based Forecasting of Modeled and Non-Modeled Inflation Variables in South Africa," Working Papers, University of Pretoria, Department of Economics 201374, University of Pretoria, Department of Economics.
  4. Pejman Bahramian & Mehmet Balcilar & Rangan Gupta & Patrick T. kanda, 2014. "Forecasting South African Inflation Using Non-Linear Models: A Weighted Loss-Based Evaluation," Working Papers, University of Pretoria, Department of Economics 201416, University of Pretoria, Department of Economics.

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