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Disentangling the Channels of the 2007–09 Recession

Open access
Stock, James H., Watson, Mark W.·2012·Brookings Papers on Economic Activity

Abstract

This paper examines the macroeconomic dynamics of the 2007–09 recession in the United States and the subsequent slow recovery. Using a dynamic factor model with 200 variables, we reach three main conclusions. First, although many of the events of the 2007–09 collapse were unprecedented, their net effect was to produce macro shocks that were larger versions of shocks previously experienced, to which the economy responded in a historically predictable way. Second, the shocks that produced the recession were primarily associated with financial disruptions and heightened uncertainty, although oil shocks played a role in the initial slowdown, and subsequent drag was added by effectively tight conventional monetary policy arising from the zero lower bound. Third, although the slow nature of the recovery is partly due to the shocks of this recession, most of the slow recovery in employment, and nearly all of the slow recovery in output, is due to a secular slowdown in trend labor force growth.

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Crossref · 10.1353/eca.2012.0005

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Metadata

Authors
Stock, J. H., & Watson, M. W.
Venue
Brookings Papers on Economic Activity
Publisher
Project MUSE
Published
2012-03
Volume
2012
Issue
1
Pages
81-135
ISSN
  • 0007-2303 (ISSN-L)
  • 1533-4465
OpenAlex
W2112938220
Cited by
776
References
2
FWCI
102.81
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Disentangling the Channels of the 2007–09 Recession. https://app.cochat.ai/papers/10.1353/eca.2012.0005
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Stock, J. H., & Watson, M. W. (2012). Disentangling the Channels of the 2007–09 Recession. Brookings Papers on Economic Activity. https://doi.org/10.1353/eca.2012.0005

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.