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Forecasting South African Macroeconomic Data with a Nonlinear DSGE Model

Author

Listed:
  • 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.

Suggested Citation

  • Mehmet Balcilar & Rangan Gupta & Kevin Kotze, 2013. "Forecasting South African Macroeconomic Data with a Nonlinear DSGE Model," Working Papers 201313, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201313
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Rangan Gupta & Patrick T. Kanda & Mampho P. Modise & Alessia Paccagnini, 2015. "DSGE model-based forecasting of modelled and nonmodelled inflation variables in South Africa," Applied Economics, Taylor & Francis Journals, vol. 47(3), pages 207-221, January.
    2. Sergey Ivashchenko, 2014. "Forecasting in a Non-Linear DSGE Model," EUSP Department of Economics Working Paper Series Ec-02/14, European University at St. Petersburg, Department of Economics.
    3. Patrick T. Kanda & Mehmet Balcilar & Pejman Bahramian & Rangan Gupta, 2016. "Forecasting South African inflation using non-linearmodels: a weighted loss-based evaluation," Applied Economics, Taylor & Francis Journals, vol. 48(26), pages 2412-2427, June.
    4. repec:ipg:wpaper:2014-471 is not listed on IDEAS
    5. Annari De Waal & Reneé Van Eyden & Rangan Gupta, 2015. "Do we need a global VAR model to forecast inflation and output in South Africa?," Applied Economics, Taylor & Francis Journals, vol. 47(25), pages 2649-2670, May.
    6. repec:ipg:wpaper:2014-562 is not listed on IDEAS
    7. Rangan Gupta & Patrick Kanda & Mampho Modise & Alessia Paccagnini, 2013. "DGSE Model-Based Forecasting of Modeled and Non-Modeled Inflation Variables in South Africa," Working Papers 259, University of Milano-Bicocca, Department of Economics, revised Nov 2013.

    More about this item

    Keywords

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

    JEL classification:

    • E0 - Macroeconomics and Monetary Economics - - General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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