The DOI matches its Crossref record.
Forecasting With Bayesian Vector Autoregressions—Five Years of Experience
Abstract
The results obtained in five years of forecasting with Bayesian vector autoregressions (BVAR's) demonstrate that this inexpensive, reproducible statistical technique is as accurate, on average, as those used by the best known commercial forecasting services. This article considers the problem of economic forecasting, the justification for the Bayesian approach, its implementation, and the performance of one small BVAR model over the past five years.
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
Predicting excess stock returns out of sample: Can anything beat the historical average?
Campbell, J. Y., & Thompson, S. B. (2008) · Review of Financial Studies · doi:10.1093/rfs/hhm055 · Cited by 2,919
Forecasting and testing in co-integrated systems
Engle, R. F., & Yoo, B. S. (1987) · Journal of Econometrics · doi:10.1016/0304-4076(87)90085-6 · Cited by 1,867
Tests of Conditional Predictive Ability
Giacomini, R., & White, H. (2006) · Econometrica · doi:10.1111/j.1468-0262.2006.00718.x · Cited by 1,646
Large Bayesian vector auto regressions
Bańbura, M., Giannone, D., & Reichlin, L. (2010) · Journal of Applied Econometrics · doi:10.1002/jae.1137 · Cited by 1,151
Prior Selection for Vector Autoregressions
Giannone, D., Lenza, M., & Primiceri, G. E. (2015) · Review of Economics and Statistics · doi:10.1162/rest_a_00483 · Cited by 710
Metadata
- Authors
- Litterman, R. B.
- Venue
- Journal of Business & Economic Statistics
- Publisher
- Informa UK Limited
- Published
- 1986-01
- Volume
- 4
- Issue
- 1
- Pages
- 25-38
- ISSN
- 0735-0015 (ISSN-L)
- 1537-2707
- OpenAlex
- W2092270509
- Cited by
- 1,137
- References
- 0
- FWCI
- 15.68
- 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: Forecasting With Bayesian Vector Autoregressions—Five Years of Experience. https://app.cochat.ai/papers/10.1080/07350015.1986.10509491
- The paper
- Litterman, R. B. (1986). Forecasting With Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics. https://doi.org/10.1080/07350015.1986.10509491
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.
