Dynamic Stochastic General Equilibrium (DSGE) models are now considered attractive by the profession not only from the theoretical perspective but also from an empirical standpoint. As a consequence of this development, methods for diagnosing the fit of these models are being proposed and implemented. In this article we illustrate how the concept of statistical identification, that was introduced and used by Spanos [Spanos, Aris, 1990. The simultaneous-equations model revisited: Statistical adequacy and identification. Journal of Econometrics 44, 87–105] to criticize traditional evaluation methods of Cowles Commission models, could be relevant for DSGE models. We conclude that the recently proposed model evaluation method, based on the DSGE−VAR(λ), might not satisfy the condition for statistical identification. However, our application also shows that the adoption of a FAVAR as a statistically identified benchmark leaves unaltered the support of the data for the DSGE model and that a DSGE-FAVAR can be an optimal forecasting model
Information on economic policy uncertainty (EPU) does matter in predicting oil returns especially when accounting for omitted nonlinearities in the relationship between these two variables via a timevarying coe¢ cient approach. In this work, we compare the forecastability of standard, Bayesian and TVP-VAR models against the random-walk and benchmark AR models. Our results indicate that over the period 1900:1-2014:2 the time-varying VAR model with stochastic volatility outranks all alternative models.JEL Classi…cation: C22, C32, C53, E60, Q41
Over the last few years, there has been a growing interest in DSGE modelling for predicting macroeconomic ‡uctuations and conducting quantitative policy analysis. Hybrid DSGE models have become popular for dealing with some of the DSGE misspeci…cations as they are able to solve the tradeo¤ between theoretical coherence and empirical …t. 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 paper, we present a state space time-varying parameter VAR model. Moreover, we focus on the DSGE-VAR that combines a micro-founded DSGE model with the ‡exibility 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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