The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Vector Autoregressions and Reality
Abstract
This article questions the statistical significance of variance decompositions and impulse response functions for unrestricted vector autoregressions. It suggests that previous authors have failed to provide confidence intervals for variance decompositions and impulse response functions. Two methods of computing such confidence intervals are developed: first, using a normal approximation; second, using bootstrapped resampling. An example from Sims's work is used to illustrate the importance of computing these confidence intervals. In this example, the 95% confidence intervals for variance decompositions span up to 66 percentage points at the usual forecasting horizon.
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
The Jackknife, the Bootstrap and Other Resampling Plans
Efron, B. (1982) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970319 · Cited by 8,120
Cited by
Karolyi, G. A. (1995) · Journal of Business & Economic Statistics · doi:10.1080/07350015.1995.10524575 · Cited by 499
The Dynamic Relationship Between Protest and Repression
Carey, S. C. (2006) · Political Research Quarterly · doi:10.1177/106591290605900101 · Cited by 405
Masih, A. M. M., & Masih, R. (1997) · Journal of Policy Modeling · doi:10.1016/s0161-8938(96)00063-4 · Cited by 398
The long-run relationship between inflation and output in postwar economies
Bullard, J., & Keating, J. W. (1995) · Journal of Monetary Economics · doi:10.1016/0304-3932(95)01227-3 · Cited by 326
Pradhan, R. P., Arvin, M. B., Hall, J. H., & Bahmani, S. (2014) · Review of Financial Economics · doi:10.1016/j.rfe.2014.07.002 · Cited by 184
Metadata
- Authors
- Runkle, D. E.
- Venue
- Journal of Business & Economic Statistics
- Publisher
- Informa UK Limited
- Published
- 2002-01
- Volume
- 20
- Issue
- 1
- Pages
- 128-133
- ISSN
- 0735-0015 (ISSN-L)
- 1537-2707
- OpenAlex
- W2021586998
- Cited by
- 413
- References
- 1
- FWCI
- 53.57
- 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: Vector Autoregressions and Reality. https://app.cochat.ai/papers/10.1198/073500102753410435
- The paper
- Runkle, D. E. (2002). Vector Autoregressions and Reality. Journal of Business & Economic Statistics. https://doi.org/10.1198/073500102753410435
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.
