Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/249861 
Year of Publication: 
2021
Series/Report no.: 
Working Paper No. 2021-25
Publisher: 
Federal Reserve Bank of Atlanta, Atlanta, GA
Abstract: 
A long tradition in macro finance studies the joint dynamics of aggregate stock returns and dividends using vector autoregressions (VARs), imposing the cross-equation restrictions implied by the Campbell-Shiller (CS) identity to sharpen inference. We take a Bayesian perspective and develop methods to draw from any posterior distribution of a VAR that encodes a priori skepticism about large amounts of return predictability while imposing the CS restrictions. In doing so, we show how a common empirical practice of omitting dividend growth from the system amounts to imposing the extra restriction that dividend growth is not persistent. We highlight that persistence in dividend growth induces a previously overlooked channel for return predictability, which we label "dividend momentum." Compared to estimation based on ordinary least squares, our restricted informative prior leads to a much more moderate, but still significant, degree of return predictability, with forecasts that are helpful out of sample and realistic asset allocation prescriptions with Sharpe ratios that outperform common benchmarks.
Subjects: 
CS restrictions
Bayesian VAR
optimal allocation
JEL: 
C32
C53
G11
G12
E47
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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