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Cited by 572

The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.

Are the responses of the U.S. economy asymmetric in energy price increases and decreases?: Are responses of the U.S. economy asymmetric?

Open access
Kilian, Lutz, Vigfusson, Robert J.·2011·Quantitative Economics
DOI 10.3982/qe99
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Abstract

How much does real gross domestic product (GDP) respond to unanticipated changes in the real price of oil? Commonly used censored oil price vector autoregressive models suggest a substantial decline in real GDP in response to unexpected increases in the real price of oil, yet no response to unexpected declines. We show that these estimates are invalid. Based on a structural model that encompasses both symmetric and asymmetric models as special cases, correctly computed impulse responses are of roughly the same magnitude in either direction, consistent with formal tests for symmetric responses. We discuss implications for theoretical models and for policy responses to energy price shocks.

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Metadata

Authors
Kilian, L., & Vigfusson, R. J.
Venue
Quantitative Economics
Publisher
The Econometric Society
Published
2011-11
Volume
2
Issue
3
Pages
419-453
ISSN
1759-7323 (ISSN-L)
DOI
10.3982/qe99
OpenAlex
W1492887676
Cited by
572
References
34
FWCI
68.43
Type
journal article
Language
en
Open access
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CoChat. (2026). Citation check: Are the responses of the U.S. economy asymmetric in energy price increases and decreases?: Are responses of the U.S. economy asymmetric? https://app.cochat.ai/papers/10.3982/qe99
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Kilian, L., & Vigfusson, R. J. (2011). Are the responses of the U.S. economy asymmetric in energy price increases and decreases?: Are responses of the U.S. economy asymmetric? Quantitative Economics. https://doi.org/10.3982/qe99

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.