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Sticky Prices in the United States
Open accessAbstract
It has often been argued that prices are sticky in the United States. However, the empirical papers that have claimed to support this view have not reflected any formal behavioral theory. This paper presents a theory that justifies price stickiness, namely, that firms, fearing to upset their customers, attribute a cost to price changes. The rational expectations equilibrium of an economy with many such firms is presented, estimated with postwar U.S. data, and tested against alternative hypotheses. The results largely support the model. Furthermore, the hypothesis that prices are not sticky is rejected by U.S. data.
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Citations
Cited by
An Estimated DSGE Model for Monetary Policy Analysis in Low-Income Countries
Peiris, S., & Saxegaard, M. (2007) · IMF Working Papers · doi:10.2139/ssrn.1078787 · Cited by 43
Financial Development and the Effectiveness of Monetary Policy Channels in Nigeria: A DSGE Approach
Apanisile, O. T., & Osinubi, T. T. (2020) · Journal of African Business · doi:10.1080/15228916.2019.1625021 · Cited by 17
Enisan, A. A., & Tolulope, A. O. (2019) · Global Business Review · doi:10.1177/0972150918825194 · Cited by 8
Akinlo, A. E., & Apanisile, O. T. (2021) · Preprint (Research Square) · doi:10.21203/rs.3.rs-317014/v1 · Cited by 5
Metadata
- Authors
- Rotemberg, J. J.
- Venue
- Journal of Political Economy
- Publisher
- University of Chicago Press
- Published
- 1982-12
- Volume
- 90
- Issue
- 6
- Pages
- 1187-1211
- ISSN
- 0022-3808 (ISSN-L)
- 1537-534X
- OpenAlex
- W2142741054
- Cited by
- 1,899
- References
- 0
- FWCI
- 20.63
- Type
- journal article
- Language
- en
- Open access
- green
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- Rotemberg, J. J. (1982). Sticky Prices in the United States. Journal of Political Economy. https://doi.org/10.1086/261117
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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.
