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On the predictability of time-varying VAR and DSGE models

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  • Stelios D. Bekiros
  • Alessia Paccagnini
Abstract
Over the last few years, there has been a growing interest in DSGE modelling for predicting macroeconomic fluctuations and conducting quantitative policy analysis. Hybrid DSGE models have become popular for dealing with some of the DSGE misspecifications as they are able to solve the trade-off between theoretical coherence and empirical fit. However, these models are still linear and they do not consider time variation for parameters. The time-varying properties in VAR or DSGE models capture the inherent nonlinearities and the adaptive underlying structure of the economy in a robust manner. In this article, we present a state-space time-varying parameter VAR model. Moreover, we focus on the DSGE–VAR that combines a microfounded DSGE model with the flexibility of a VAR framework. All the aforementioned models as well simple DSGEs and Bayesian VARs are used in a comparative investigation of their out-of-sample predictive performance regarding the US economy. The results indicate that while in general the classical VAR and BVARs provide with good forecasting results, in many cases the TVP–VAR and the DSGE–VAR outperform the other models.

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  • Stelios D. Bekiros & Alessia Paccagnini, 2013. "On the predictability of time-varying VAR and DSGE models," Open Access publications 10197/7326, School of Economics, University College Dublin.
    • Handle: RePEc:ucn:oapubs:10197/7326
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    5. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    6. Bekiros, Stelios D. & Paccagnini, Alessia, 2015. "Macroprudential Policy And Forecasting Using Hybrid Dsge Models With Financial Frictions And State Space Markov-Switching Tvp-Vars," Macroeconomic Dynamics, Cambridge University Press, vol. 19(7), pages 1565-1592, October.
    7. Maddalena Cavicchioli, 2020. "Invertibility and VAR Representations of Time-Varying Dynamic Stochastic General Equilibrium Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 61-86, January.
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    9. Alessia Paccagnini, 2012. "Comparing Hybrid DSGE Models," Working Papers 228, University of Milano-Bicocca, Department of Economics, revised Dec 2012.
    10. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2017. "The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(2), pages 109-121, March.
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    12. David Hudgins & Patrick M. Crowley, 2019. "Stress-Testing U.S. Macroeconomic Policy: A Computational Approach Using Stochastic and Robust Designs in a Wavelet-Based Optimal Control Framework," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1509-1546, April.
    13. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers 1210, University of Nevada, Las Vegas , Department of Economics.
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    More about this item

    Keywords

    Hybrid DSGE; Time-varying VAR; Kalman filter; Bayesian VAR; Forecasting;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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