The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Scoring the Leading Indicators
Abstract
The authors evaluate the ability of the composite index of leading indicators to predict business cycle turning points. Formal probability-assessment scoring rules are applied to turning-point probabilities generated from the leading index via a Bayesian sequential probability recursion. These scoring rules enable rigorous and systematic evaluation of leading indicator forecasts. The results are used to assess the merits of forecasting with the composite leading index and to suggest possible improvements in its construction. Copyright 1989 by the University of Chicago.
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Citations
Cites
Scoring Rules and the Evaluation of Probability Assessors
Winkler, R. L. (1969) · Journal of the American Statistical Association · doi:10.1080/01621459.1969.10501037 · Cited by 271
Verification of forecasts expressed in terms of probability
BRIER, G. W. (1950) · Monthly Weather Review · doi:10.1175/1520-0493(1950)078%3c0001:vofeit%3e2.0.co;2 · cited 1×
“The probability-forecast analog of mean squared error is Brier's (1950) quadratic probability score:8”
Method
Metadata
- Authors
- Diebold, F. X., & Rudebusch, G. D.
- Venue
- The Journal of Business
- Publisher
- University of Chicago Press
- Published
- 1989-01
- Volume
- 62
- Issue
- 3
- Pages
- 369
- ISSN
- 0021-9398 (ISSN-L)
- 1537-5374
- OpenAlex
- W2015943086
- Cited by
- 350
- References
- 2
- FWCI
- 6.53
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Scoring the Leading Indicators. https://app.cochat.ai/papers/10.1086/296467
- The paper
- Diebold, F. X., & Rudebusch, G. D. (1989). Scoring the Leading Indicators. The Journal of Business. https://doi.org/10.1086/296467
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.
