Estimating point and density forecasts for the US economy with a factor-augmented vector autoregressive DSGE model

Although policymakers and practitioners are particularly interested in dynamic stochastic general equilibrium (DSGE) models, these are typically too stylized to be applied directly to the data and often yield weak prediction results. Very recently, hybrid DSGE models have become popular for dealing with some of the model misspecifications. Major advances in estimation methodology could allow these models to outperform well-known time series models and effectively deal with more complex real-world problems as richer sources of data become available. In this study we introduce a Bayesian approach to estimate a novel factor augmented DSGE model that extends the model of Consolo et al. [Consolo, A., Favero, C.A., and Paccagnini, A., 2009. On the Statistical Identification of DSGE Models. Journal of Econometrics, 150, 99–115]. We perform a comparative predictive evaluation of point and density forecasts for many different specifications of estimated DSGE models and various classes of VAR models, using datasets from the US economy including real-time data. Simple and hybrid DSGE models are implemented, such as DSGE-VAR and tested against standard, Bayesian and factor augmented VARs. The results can be useful for macro-forecasting and monetary policy analysis.