The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Combining forecast densities from VARs with uncertain instabilities
Abstract
Recursive‐weight forecast combination is often found to an ineffective method of improving point forecast accuracy in the presence of uncertain instabilities. We examine the effectiveness of this strategy for forecast densities using (many) vector autoregressive (VAR) and autoregressive (AR) models of output growth, inflation and interest rates. Our proposed recursive‐weight density combination strategy, based on the recursive logarithmic score of the forecast densities, produces well‐calibrated predictive densities for US real‐time data by giving substantial weight to models that allow for structural breaks. In contrast, equal‐weight combinations produce poorly calibrated forecast densities for Great Moderation data. Copyright © 2010 John Wiley & Sons, Ltd.
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
Dawid, A. P. (1984) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2981683 · Cited by 1,085 · cited 1×
“A popular method, following Rosenblatt (1952), Dawid (1984) and Diebold et al. (1998), evaluates using the probability integral transforms (pits) of the realization of the variable with respect to the forecast densities.”
Method
Cited by
Clark, T. E., & Ravazzolo, F. (2015) · Journal of Applied Econometrics · doi:10.1002/jae.2379 · Cited by 317
Forecast combinations: An over 50-year review
Wang, X., Hyndman, R. J., Li, F., & Kang, Y. (2023) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2022.11.005 · Cited by 280
Metadata
- Authors
- Jore, A. S., Mitchell, J., & Vahey, S. P.
- Venue
- Journal of Applied Econometrics
- Publisher
- Wiley
- Published
- 2010-05-04
- Volume
- 25
- Issue
- 4
- Pages
- 621-634
- ISSN
- 0883-7252 (ISSN-L)
- 1099-1255
- OpenAlex
- W2156380231
- Cited by
- 180
- References
- 1
- FWCI
- 27.03
- 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: Combining forecast densities from VARs with uncertain instabilities. https://app.cochat.ai/papers/10.1002/jae.1162
- The paper
- Jore, A. S., Mitchell, J., & Vahey, S. P. (2010). Combining forecast densities from VARs with uncertain instabilities. Journal of Applied Econometrics. https://doi.org/10.1002/jae.1162
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.
