The DOI matches its Crossref record.
Optimal Inference in Cointegrated Systems
Abstract
Properties of maximum likelihood estimates of cointegrated systems are studied. Alternative formulations are considered, including a new triangular system error correction mechanism. We demonstrate that full system maximum likelihood brings the problem of inference within the family covered by the locally asymptotically mixed normal asymptotic theory, provided all unit roots have been eliminated by specification and data transformation. Methodological issues provide a major focus of the paper. Our results favor use of full system estimation in error correction mechanisms or subsystem methods that are asymptotically equivalent. They also point to disadvantages in the use of unrestricted VAR's formulated in levels and of certain single equation approaches to estimation of error correction mechanisms. Copyright 1991 by The Econometric Society.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Johansen, S., & Juselius, K. (1990) · Oxford Bulletin of Economics and Statistics · doi:10.1111/j.1468-0084.1990.mp52002003.x · Cited by 14,051
Pitfalls and Opportunities: What Macroeconomists Should Know about Unit Roots
Campbell, J. Y., & Perron, P. (1991) · NBER Macroeconomics Annual · doi:10.1086/654163 · Cited by 1,255
Estimating Long-Run Economic Equilibria
Phillips, P. C. B., & Loretan, M. (1991) · The Review of Economic Studies · doi:10.2307/2298004 · Cited by 853
Self-citationInternational evidence on the stock market and aggregate economic activity
Cheung, Y.-W., & Ng, L. K. (1998) · Journal of Empirical Finance · doi:10.1016/s0927-5398(97)00025-x · Cited by 329
Identifying cointegration by eigenanalysis
Zhang, R., Robinson, P., & Yao, Q. (2019) · Journal of the American Statistical Association · doi:10.1080/01621459.2018.1458620 · Cited by 51
Metadata
- Authors
- Phillips, P. C. B.
- Venue
- Econometrica
- Publisher
- JSTOR
- Published
- 1991-03
- Volume
- 59
- Issue
- 2
- Pages
- 283
- ISSN
- 0012-9682 (ISSN-L)
- OpenAlex
- W2164599393
- Cited by
- 1,099
- References
- 0
- FWCI
- 127.71
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Optimal Inference in Cointegrated Systems. https://app.cochat.ai/papers/10.2307/2938258
- The paper
- Phillips, P. C. B. (1991). Optimal Inference in Cointegrated Systems. Econometrica. https://doi.org/10.2307/2938258
Data sources
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.
