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Cited by 874

The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.

Business Cycle Accounting

Chari, V. V., Kehoe, Patrick J., McGrattan, Ellen R.·2007·Econometrica

Abstract

We propose a simple method to help researchers develop quantitative models of economic fluctuations. The method rests on the insight that many models are equivalent to a prototype growth model with time-varying wedges that resemble productivity, labor and investment taxes, and government consumption. Wedges that correspond to these variables—efficiency, labor, investment, and government consumption wedges—are measured and then fed back into the model so as to assess the fraction of various fluctuations they account for. Applying this method to U.S. data for the Great Depression and the 1982 recession reveals that the efficiency and labor wedges together account for essentially all of the fluctuations; the investment wedge plays a decidedly tertiary role, and the government consumption wedge plays none. Analyses of the entire postwar period and alternative model specifications support these results. Models with frictions manifested primarily as investment wedges are thus not promising for the study of U.S. business cycles.

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Crossref · 10.1111/j.1468-0262.2007.00768.x

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Metadata

Authors
Chari, V. V., Kehoe, P. J., & McGrattan, E. R.
Venue
Econometrica
Published
2007-05
Volume
75
Issue
3
Pages
781-836
ISSN
  • 0012-9682 (ISSN-L)
  • 1468-0262
OpenAlex
W2105268279
Cited by
874
References
38
FWCI
137.64
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Business Cycle Accounting. https://app.cochat.ai/papers/10.1111/j.1468-0262.2007.00768.x
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Chari, V. V., Kehoe, P. J., & McGrattan, E. R. (2007). Business Cycle Accounting. Econometrica. https://doi.org/10.1111/j.1468-0262.2007.00768.x

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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.