Data-Rich DSGE Model Forecasts of the Great Recession and its Recovery

I investigate the extent to which modern Dynamic Stochastic General Equilibrium (DSGE) models can produce labor market dynamics in response to a financial crisis that are consistent with the experience of the Great Recession. Using the methods of Boivin and Giannoni (2006) and Kryshko (2011), I estimate two DSGE models in a data-rich environment. This allows me to examine the dynamics of economic series not obtainable in traditional DSGE model estimation. I find that negative financial shocks are associated with longer recoveries in real investment, capital intensive sectors of the labor market and average unemployment duration when compared to other negative output shocks. These results hold when the recession magnitude is normalized across the shocks. The two models estimated in this paper include close variations of the Smets & Wouters (2003, 2007) New Keynesian model and the FRBNY (Del Negro et al. 2013) model that augments the Smets & Wouters model with a financial accelerator. I find the FRBNY model with a financial accelerator is equipped with better tools to identify the dynamics associated with the Great Recession and its recovery in regard to many labor and financial metrics including the unemployment rate, total number of employees by sector and business loans.