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Predictive performance of DSGE model for small open economy - the case study of Czech Republic

Author

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  • Tomáš Jeřábek

    (Department of economy and management, College of Business and Hotel Management. Bosonožská 9, 625 00 Brno, Czech Republic)

  • Jakub Trojan

    (Institute of Geography, Faculty of Science, Masaryk University. Kotlářská 2, 611 37 Brno, Czech Republic)

  • Radka Šperková

    (Department of economy and management, College of Business and Hotel Management. Bosonožská 9, 625 00 Brno, Czech Republic)

Abstract

Multivariate time series forecasting is applied in a wide range of economic activities related to regional competitiveness and is the basis of almost all macroeconomic analysis. From the point of view of political practice is appropriate to seek a model that reached a quality prediction performance for all the variables. As monitored variables were used GDP growth, inflation and interest rates. The paper focuses on performance prediction evaluation of the small open economy New Keynesian DSGE model for the Czech republic, where Bayesian method are used for their parameters estimation, against different types of Bayesian and naive random walk model. The performance of models is identified using historical dates including domestic economy and foreign economy, which is represented by countries of the Eurozone. The results indicate that the DSGE model generates estimates that are competitive with other models used in this paper.

Suggested Citation

  • Tomáš Jeřábek & Jakub Trojan & Radka Šperková, 2013. "Predictive performance of DSGE model for small open economy - the case study of Czech Republic," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 61(7), pages 2229-2238.
    • Handle: RePEc:mup:actaun:actaun_2013061072229
    DOI: 10.11118/actaun201361072229
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    1. Tomáš Jeřábek & Radka Šperková, 2015. "A Predictive Likelihood Approach to Bayesian Averaging," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 63(4), pages 1269-1276.

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