{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T16:25:15Z","timestamp":1788193515849,"version":"build-2803163510"},"reference-count":48,"publisher":"Walter de Gruyter GmbH","issue":"2","funder":[{"DOI":"10.13039\/501100002428","name":"Austrian Science Fund","doi-asserted-by":"publisher","award":["DOC23-G16 VGSF"],"award-info":[{"award-number":["DOC23-G16 VGSF"]}],"id":[{"id":"10.13039\/501100002428","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    This article considers a stable vector autoregressive (VAR) model and investigates return predictability in a Bayesian context. The bivariate VAR system comprises asset returns and a further prediction variable, such as the dividend-price ratio, and allows pinning down the question of return predictability to the value of one particular model parameter. We develop a new shrinkage type prior for this parameter and compare our Bayesian approach to ordinary least squares estimation and to the reduced-bias estimator proposed in Amihud and Hurvich (2004. \u201cPredictive Regressions: A Reduced-Bias Estimation Method.\u201d\n                    <jats:italic>Journal of Financial and Quantitative Analysis<\/jats:italic>\n                    39: 813\u201341). A simulation study shows that the Bayesian approach dominates the reduced-bias estimator in terms of observed size (false positive) and power (false negative). We apply our methodology to a system comprising annual CRSP value-weighted returns running, respectively, from 1926 to 2004 and from 1953 to 2021, and the logarithmic dividend-price ratio. For the first sample, the Bayesian approach supports the hypothesis of no return predictability, while for the second data set weak evidence for predictability is observed. Then, instead of the dividend-price ratio, some prediction variables proposed in Welch and Goyal (2008. \u201cA Comprehensive Look at the Empirical Performance of Equity Premium Prediction.\u201d\n                    <jats:italic>Review of Financial Studies<\/jats:italic>\n                    21: 1455\u2013508) are used. Also with these prediction variables, only weak evidence for return predictability is supported by Bayesian testing. These results are corroborated with an out-of-sample forecasting analysis.\n                  <\/jats:p>","DOI":"10.1515\/snde-2022-0110","type":"journal-article","created":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T02:21:27Z","timestamp":1703557287000},"page":"337-378","source":"Crossref","is-referenced-by-count":1,"title":["Bayesian Reconciliation of Return Predictability"],"prefix":"10.1515","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9506-3471","authenticated-orcid":false,"given":"Borys","family":"Koval","sequence":"first","affiliation":[{"name":"Vienna Graduate School of Finance , WU Vienna University of Economics and Business , 1020 Vienna , Austria"},{"name":"Department of Economics and Finance , Institute for Advanced Studies , 1080 Vienna , Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sylvia","family":"Fr\u00fchwirth-Schnatter","sequence":"additional","affiliation":[{"name":"Department of Finance, Accounting and Statistics , WU Vienna University of Economics and Business , 1020 Vienna , Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leopold","family":"S\u00f6gner","sequence":"additional","affiliation":[{"name":"Department of Economics and Finance , Institute for Advanced Studies , 1080 Vienna , Austria"},{"name":"Vienna Graduate School of Finance, WU Vienna University of Economics and Business , 1020 Vienna , Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2023,12,25]]},"reference":[{"key":"2025121215590471114_j_snde-2022-0110_ref_001","doi-asserted-by":"crossref","unstructured":"Amihud, Y., and C. M. Hurvich. 2004. \u201cPredictive Regressions: A Reduced-Bias Estimation Method.\u201d Journal of Financial and Quantitative Analysis 39: 813\u201341. https:\/\/doi.org\/10.1017\/s0022109000003227.","DOI":"10.1017\/S0022109000003227"},{"key":"2025121215590471114_j_snde-2022-0110_ref_002","doi-asserted-by":"crossref","unstructured":"Andrews, D. W. K. 1991. \u201cHeteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation.\u201d Econometrica 59: 817\u201354. https:\/\/doi.org\/10.2307\/2938229.","DOI":"10.2307\/2938229"},{"key":"2025121215590471114_j_snde-2022-0110_ref_003","doi-asserted-by":"crossref","unstructured":"Ba\u015ft\u00fcrk, N., L. Hoogerheide, and H. K. van Dijk. 2017. \u201cBayesian Analysis of Boundary and Near-Boundary Evidence in Econometric Models with Reduced Rank.\u201d Bayesian Analysis 12: 879\u2013917. https:\/\/doi.org\/10.1214\/17-ba1061.","DOI":"10.1214\/17-BA1061"},{"key":"2025121215590471114_j_snde-2022-0110_ref_004","doi-asserted-by":"crossref","unstructured":"Barberis, N. 2000. \u201cInvesting for the Long Run when Returns are Predictable.\u201d The Journal of Finance 55: 225\u201364. https:\/\/doi.org\/10.1111\/0022-1082.00205.","DOI":"10.1111\/0022-1082.00205"},{"key":"2025121215590471114_j_snde-2022-0110_ref_005","doi-asserted-by":"crossref","unstructured":"Berger, J. O., and R. Y. Yang. 1994. \u201cNoninformative Priors and Bayesian Testing for the AR(1) Model.\u201d Econometric Theory 10: 461\u201382. https:\/\/doi.org\/10.1017\/s026646660000863x.","DOI":"10.1017\/S026646660000863X"},{"key":"2025121215590471114_j_snde-2022-0110_ref_006","doi-asserted-by":"crossref","unstructured":"Cadonna, A., S. 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