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Repeated Choice: A Theory of Stochastic Intertemporal Preferences

Saito, Kota and Lu, Jay (2020) Repeated Choice: A Theory of Stochastic Intertemporal Preferences. Social Science Working Paper, 1449. California Institute of Technology , Pasadena, CA. (Unpublished)

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We provide a repeated-choice foundation for stochastic choice. We obtain necessary and sufficient conditions under which an agent's observed stochastic choice can be represented as a limit frequency of optimal choices over time. In our model, the agent repeatedly chooses today's consumption and tomorrow's continuation menu, aware that future preferences will evolve according to a subjective ergodic utility process. Using our model, we demonstrate how not taking into account the intertemporal structure of the problem may lead an analyst to biased estimates of risk preferences. Estimation of preferences can be performed by the analyst without explicitly modeling continuation problems (i.e. stochastic choice is independent of continuation menus) if and only ifthe utility process takes on the standard additive and separable form. Applications include dynamic discrete choice models when agents have non-trivial intertemporal preferences, such as Epstein-Zin preferences. We provide a numerical example which shows the significance of biases caused by ignoring the agent's Epstein-Zin preferences.

Item Type:Report or Paper (Working Paper)
Saito, Kota0000-0003-1189-8912
Additional Information:We want to thank Andy Atkeson, Simon Board, Larry Epstein, Mira Frick, Faruk Gul, Ryota Iijima, Asen Kochov, Bart Lipman, Rosa Matzkin, Tomasz Strzalecki, John Rust, Yi Xing and seminar audiences at the SAET Conference at Academia Sinica, CIREQ Montreal Micro Theory Conference, Georgetown, Maryland, University of Montreal, Michigan, Rochester, University of Tokyo, LSE, Yale, LA Theory Conference, Caltech Junior Theory Workshop, RUD Conference at PSE, Zurich, Queen Mary, Royal Holloway, UCL, Pittsburg-Carnegie Mellon, Penn State, Boston University, and the Ohio state university for their helpful comments. We especially thank Kim Border for several discussions which helped us with the axiomatic characterization of the model We also thank Jeffrey Zeidel for his excellent research assistance for the numerical result. Financial support from the NSF under awards SES-1558757 (Saito), SES-1919263 (Saito) and SES-1919275 (Lu) are gratefully acknowledged.
Group:Social Science Working Papers
Funding AgencyGrant Number
Series Name:Social Science Working Paper
Issue or Number:1449
Record Number:CaltechAUTHORS:20200106-083810655
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:100525
Deposited By: Hanna Storlie
Deposited On:07 Jan 2020 19:36
Last Modified:07 Jan 2020 19:36

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