Policy-Oriented Macroeconomic Forecasting With Hybrid DSGE and Time-Varying Parameter VAR Models

Micro-founded dynamic stochastic general equilibrium (DSGE) models appear to be particularly suited for evaluating the consequences of alternative macroeconomic policies. Recently, increasing efforts have been undertaken by policymakers to use these models for forecasting, although this proved to be problematic, due to estimation and identification issues. Hybrid DSGE models have become popular for dealing with some of model misspeci fications and the trade-off between theoretical coherence and empirical fi t, thus allowing them to compete in terms of predictability with VAR models. However, DSGE and VAR models are still linear, and they do not consider time-variation in parameters that could account for inherent non-linearities and capture the adaptive underlying structure of the economy in a robust manner. This study conducts a comparative evaluation of the out-of-sample predictive performance of many different speci fications of DSGE models and various classes of VAR models; using data-sets for the real GDP, the harmonized CPI, and the nominal short-term interest rate series in the Euro area. Simple and hybrid DSGE models were implemented; including DSGE-VAR and Factor Augmented DGSE; and tested against standard, Bayesian, and Factor Augmented VARs. Moreover, a new state-space time-varying VAR model is presented. The total period spanned from 1970:1 to 2010:4, with an out-of-sample testing period of 2006:1-2010:4, which covers the global financial crisis and the EU debt crisis. The results of this study can be useful in conducting monetary policy analysis and macro-forecasting in the Euro area