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Pitfalls and Opportunities: What Macroeconomists Should Know about Unit Roots

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Campbell, John Y., Perron, Pierre·1991·NBER Macroeconomics Annual

Abstract

This paper is an introduction to unit root econometrics as applied in macroeconomics. The paper first discusses univariate time series analysis, emphasizing the following topics: alternative representations of unit root processes, unit root testing procedures, the power of unit root tests, and the interpretation of unit root econometrics in finite samples. A second part of the paper tackles similar issues in a multivariate context where cointegration is now the central concept. The paper reviews representation, testing, and estimation of multivariate time series models with some unit roots. Two important themes of this paper are first, the importance of correctly specifying deterministic components of the series, and second, the usefulness of unit root tests not as methods to uncover some "true relation" but as practical devices that can be used to impose reasonable restrictions on the data and to suggest what asymptotic distribution theory gives the best approximation to the finite-sample distribution of coefficient estimates and test statistics.

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Metadata

Authors
Campbell, J. Y., & Perron, P.
Venue
NBER Macroeconomics Annual
Published
1991-01
Volume
6
Pages
141-201
ISSN
  • 0889-3365 (ISSN-L)
  • 1537-2642
OpenAlex
W3122245991
Cited by
1,255
References
84
FWCI
74.40
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Pitfalls and Opportunities: What Macroeconomists Should Know about Unit Roots. https://app.cochat.ai/papers/10.1086/654163
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Campbell, J. Y., & Perron, P. (1991). Pitfalls and Opportunities: What Macroeconomists Should Know about Unit Roots. NBER Macroeconomics Annual. https://doi.org/10.1086/654163

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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.