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

Sacha Gelfer

Review of Economic Dynamics, 2019, vol. 32, 18-41

Abstract: I investigate the extent to which modern dynamic stochastic general equilibrium (DSGE) models can produce macroeconomic and 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. The two models estimated in this paper include close variations of the Smets and 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 model with a financial accelerator that is estimated in a data-rich environment is able to significantly out-forecast modern DSGE models not estimated in a data-rich environment and the Survey of Professional Forecasters (SPF) in regard to core macroeconomic growth variables and many labor and financial metrics including the unemployment rate, total number of employees by sector and business loans. (Copyright: Elsevier)

Keywords: Data-rich DSGE; DSGE-DFM; Financial accelerator; Forecast evaluation (search for similar items in EconPapers)
JEL-codes: C53 E17 E27 E37 E44 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (14)

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DOI: 10.1016/j.red.2018.12.005

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