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Cited by 466

The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.

Pooling of forecasts

Open access
Hendry, David F., Clements, Michael P.·2004·The Econometrics Journal
DOI 10.1111/j.1368-423x.2004.00119.x
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Abstract

We consider forecasting using a combination, when no model coincides with a non‐constant data generation process (DGP). Practical experience suggests that combining forecasts adds value, and can even dominate the best individual device. We show why this can occur when forecasting models are differentially mis‐specified, and is likely to occur when the DGP is subject to location shifts. Moreover, averaging may then dominate over estimated weights in the combination. Finally, it cannot be proved that only non‐encompassed devices should be retained in the combination. Empirical and Monte Carlo illustrations confirm the analysis.

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Crossref · 10.1111/j.1368-423x.2004.00119.x

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Metadata

Authors
Hendry, D. F., & Clements, M. P.
Venue
The Econometrics Journal
Publisher
Oxford University Press (OUP)
Published
2004-06-01
Volume
7
Issue
1
Pages
1-31
ISSN
  • 1368-4221 (ISSN-L)
  • 1368-423X
DOI
10.1111/j.1368-423x.2004.00119.x
OpenAlex
W2121286295
Cited by
466
References
35
FWCI
55.54
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Pooling of forecasts. https://app.cochat.ai/papers/10.1111/j.1368-423x.2004.00119.x
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Hendry, D. F., & Clements, M. P. (2004). Pooling of forecasts. The Econometrics Journal. https://doi.org/10.1111/j.1368-423x.2004.00119.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.