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International Journal of Forecasting
Article . 2019 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
SSRN Electronic Journal
Article . 2018 . Peer-reviewed
Data sources: Crossref
EconStor
Research . 2018
Data sources: EconStor
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DSGE Forecasts of the Lost Recovery

Authors: Cai, Michael; Del Negro, Marco; Giannoni, Marc P.; Gupta, Abhi; Li, Pearl; Moszkowski, Erica;

DSGE Forecasts of the Lost Recovery

Abstract

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.

Keywords

financial frictions, ddc:330, DSGE models, E44, Great Recession, C32, C54, real-time forecasts, C11, E43

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
22
Top 10%
Top 10%
Top 10%
bronze