The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Impulse response and forecast error variance asymptotics in nonstationary VARs
Open accessAbstract
Estimated impulse responses and forecast error decompositions are shown to be inconsistent at long horizons in unrestricted VARs with some unit roots. Predictions from unrestricted VARs also do not converge to the optimal predictors over long forecast horizons. In contrast, reduced rank regressions produce impulse responses and forecast error variance estimates that are consistent and predictions that are asymptotically optimal, provided the cointegrating rank is correctly specified or consistently estimated by an order selector such as PIC. Some simulations show these findings to be relevant in finite samples in VARs with some unit roots and cointegration.
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
Cites
STATISTICAL ANALYSIS OF COINTEGRATION VECTORS
Johansen, S. (1988) · Journal of Economic Dynamics and Control · doi:10.1016/0165-1889(88)90041-3 · Cited by 17,027
Cited by
Export-led growth: a survey of the empirical literature and some non-causality results. Part 1
A. Giles, J., & Williams, C. L. (2000) · The Journal of International Trade & Economic Development · doi:10.1080/09638190050086177 · Cited by 473
Local Projection Inference Is Simpler and More Robust Than You Think
Montiel Olea, J. L., & Plagborg-Møller, M. (2021) · Econometrica · doi:10.3982/ecta18756 · Cited by 437
INFRASTRUCTURE, LONG‐RUN ECONOMIC GROWTH AND CAUSALITY TESTS FOR COINTEGRATED PANELS
CANNING, D., & PEDRONI, P. (2008) · The Manchester School · doi:10.1111/j.1467-9957.2008.01073.x · Cited by 421
Metadata
- Authors
- Phillips, P. C. B.
- Venue
- Journal of Econometrics
- Publisher
- Elsevier BV
- Published
- 1998-03
- Volume
- 83
- Issue
- 1-2
- Pages
- 21-56
- ISSN
- 0304-4076 (ISSN-L)
- OpenAlex
- W1980289683
- Cited by
- 309
- References
- 1
- FWCI
- 21.02
- Type
- journal article
- Language
- en
- Open access
- hybrid
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: Impulse response and forecast error variance asymptotics in nonstationary VARs. https://app.cochat.ai/papers/10.1016/s0304-4076(97)00064-x
- The paper
- Phillips, P. C. B. (1998). Impulse response and forecast error variance asymptotics in nonstationary VARs. Journal of Econometrics. https://doi.org/10.1016/s0304-4076(97)00064-x
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.