The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Predictive regressions
Open accessAbstract
When a rate of return is regressed on a lagged stochastic regressor, such as a dividend yield, the regression disturbance is correlated with the regressor's innovation. The OLS estimator's finite-sample properties, derived here, can depart substantially from the standard regression setting. Bayesian posterior distributions for the regression parameters are obtained under specifications that differ with respect to (i) prior beliefs about the autocorrelation of the regressor and (ii) whether the initial observation of the regressor is specified as fixed or stochastic. The posteriors differ across such specifications, and asset allocations in the presence of estimation risk exhibit sensitivity to those differences.
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Citations
Cites
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Cited by
Investor Sentiment and the Cross‐Section of Stock Returns
BAKER, M., & WURGLER, J. (2006) · The Journal of Finance · doi:10.1111/j.1540-6261.2006.00885.x · Cited by 6,049
A comprehensive look at the empirical performance of equity premium prediction
Welch, I., & Goyal, A. (2008) · Review of Financial Studies · doi:10.1093/rfs/hhm014 · Cited by 3,987
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
“Many of the predictor variables in the literature are highly persistent: Nelson and Kim (1993) and Stambaugh (1999) pointed out that persistence leads to biased coe!cients in predictive regressions if innovations in the predictor variable are correlated with returns (as is strongly the case for…”
BackgroundThe Dog That Did Not Bark: A Defense of Return Predictability
Cochrane, J. H. (2008) · Review of Financial Studies · doi:10.1093/rfs/hhm046 · Cited by 1,227
Forecasting the equity risk premium: the role of technical indicators
Neely, C. J., Rapach, D. E., Tu, J., & Zhou, G. (2014) · Management Science · doi:10.1287/mnsc.2013.1838 · Cited by 1,003
Metadata
- Authors
- Stambaugh, R. F.
- Venue
- Journal of Financial Economics
- Publisher
- Elsevier BV
- Published
- 1999-12
- Volume
- 54
- Issue
- 3
- Pages
- 375-421
- ISSN
- 0304-405X (ISSN-L)
- OpenAlex
- W4247747078
- Cited by
- 1,595
- References
- 1
- FWCI
- 39.65
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Stambaugh, R. F. (1999). Predictive regressions. Journal of Financial Economics. https://doi.org/10.1016/s0304-405x(99)00041-0
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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.
