Papers

DSGE Forecasts of the Lost Recovery
with Marco Del Negro, Marc P. Giannoni, Abhi Gupta, Pearl Li, & Erica Moszkowski
International Journal of Forecasting (2019), forthcoming.

The years following the Great Recession were challenging for forecasters. Unlike other deep downturns, this recession was not followed by a swift recovery, but generated a sizable and persistent output gap that was not accompanied by deflation as a traditional Phillips curve relationship would have predicted. Moreover, the zero lower bound and unconventional monetary policy generated an unprecedented policy environment. We document the real real-time forecasting performance of the New York Fed dynamic stochastic general equilibrium (DSGE) model during this period and explain the results using the pseudo real-time forecasting performance results from a battery of DSGE models. We find the New York Fed DSGE model’s forecasting accuracy to be comparable to that of private forecasters and notably better, for output growth, than the median forecasts from the Federal Open Market Committee’s Summary of Economic Projections. The model’s financial frictions were key in obtaining these results as they implied a slow recovery following the financial crisis.

Works in Progress

Online Estimation of DSGE Models
with Marco Del Negro, Edward Herbst, Ethan Matlin, Reca Sarfati, and Frank Schorfheide

This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, explore the benefits of generalized data tempering for “online” or “real-time” estimation, and provide examples of multimodal posteriors that are well captured by SMC methods. We then use the online estimation of the DSGE model to compute pseudo-out-of-sample density forecasts and document the benefits of conditioning DSGE model forecasts on nowcasts of macroeconomic variables and interest rate expectations pre and post Great Recession and we compare the predictive performance of DSGE models based on “standard” and more diffuse prior distributions.