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Judging statistical models of individual decision making under risk using in- and out-of-sample criteria

Drichoutis, Andreas and Lusk, Jayson (2012): Judging statistical models of individual decision making under risk using in- and out-of-sample criteria.

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Abstract

Despite the fact that conceptual models of individual decision making under risk are deterministic, attempts to econometrically estimate risk preferences require some assumption about the stochastic nature of choice. Unfortunately, the consequences of making different assumptions are, at present, unclear. In this paper, we compare two popular error specifications (Luce vs. Fechner), with and without accounting for contextual utility, for two different conceptual models (expected utility and rank-dependent expected utility) using in- and out-of-sample selection criteria. We find drastically different inferences about structural risk preferences across the competing specifications. Overall, a mixture model combining the two conceptual models assuming Fechner error and contextual utility provides the best fit of the data both in- and out-of-sample.

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