Dealing with financial instability under a DSGE modeling approach with banking intermediation : a forecastability analysis versus TVP-VARs

Abstract

Recently there has been an increasing awareness on the role that the banking sector can play in macroeconomic activity, especially within the context of the DSGE literature. In this work, we present a DSGE model with financial intermediation as in Gertler and Karadi (2011). The estimation of the shocks and of the structural parameters shows that time-variation can be crucial in the empirical analysis. As DSGE modeling fails to take into account inherent nonlinearities of the economy, we introduce a novel time-varying coefficient state-space estimation method for VAR processes, for homoskedastic and heteroskedastic error structures (TVP-VAR). We conduct an extensive empirical exercise to compare the out-of-sample forecastability of the DSGE model versus standard ARs, VARs, Bayesian VARs and TVP-VARs. We find that the TVP-VAR provides the best forecasting performance for the series of GDP and net worth of financial intermediaries for all steps-ahead, while the DSGE model with the incorporation of a banking sector outperforms the other specifications in forecasting inflation and the federal funds rate at shorter horizons.