Sources of Entropy in Representative Agent Models





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    • Backus and Zin are from New York University and NBER, and Chernov is from UCLA Anderson School and CEPR. We are grateful to many people for help with this project, including: Jarda Borovicka, Nina Boyarchenko, Adam Brandenburger, Wayne Ferson, Lars Hansen, Christian Heyerdahl-Larsen, Hanno Lustig, Ian Martin, Monika Piazzesi, Bryan Routledge, Andrea Tamoni, and Harald Uhlig as well as participants in seminars at, and conferences sponsored by, AHL, CEPR, CERGE, Columbia, CREATES/SoFiE, Duke, ECB, Federal Reserve Board, Federal Reserve Banks of Atlanta, Minneapolis, and San Francisco, Geneva, IE Business School, LSE, LUISS Guido Carli University, Minnesota, NBER, NYU, Penn State, Reading, SED, SIFR, and USC. We also thank Campbell Harvey, an Associate Editor, and two referees for helpful comments on earlier versions.


We propose two data-based performance measures for asset pricing models and apply them to models with recursive utility and habits. Excess returns on risky securities are reflected in the pricing kernel's dispersion and riskless bond yields are reflected in its dynamics. We measure dispersion with entropy and dynamics with horizon dependence, the difference between entropy over several periods and one. We compare their magnitudes to estimates derived from asset returns. This exercise reveals tension between a model's ability to generate one-period entropy, which should be large, and horizon dependence, which should be small.