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.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: error specification; expected utility theory; experiment; probability weighting; rank dependent utility; risk
• Subjects: D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D81 - Criteria for Decision-Making under Risk and Uncertainty; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C25 - Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions ; Probabilities; C - Mathematical and Quantitative Methods > C9 - Design of Experiments > C91 - Laboratory, Individual Behavior

References

Andersen, S., G. W. Harrison, M. I. Lau, and E. E. Rutstrom (2008). Eliciting risk and time preferences. Econometrica 76 (3), 583-618.

Andersen, S., G. W. Harrison, M. I. Lau, and E. E. Rutstrom (2011). Discounting behavior: A reconsideration. Center for the Economic Analysis of Risk, Working Paper 2011-03 .

Carbone, E. and J. Hey (2000). Which error story is best? Journal of Risk and Uncertainty 20 (2), 161-176.

Clarke, K. A. (2003). Nonparametric model discrimination in international relations. Journal of Conflict Resolution 47 (1), 72-93.

Erdem, T. (1996). A dynamic analysis of market structure based on panel data. Marketing Science 15 (4), 359-378.

Fechner, G. (1860/1966). Elements of Psychophysics. New York: Henry Holt.

Fischbacher, U. (2007). z-tree: Zurich toolbox for ready-made economic experiments. Experimental Economics 10 (2), 171-178.

Greiner, B. (2004). An online recruitment system for economic experiments. In K. Kremer and V. Macho (Eds.), Forschung Und Wissenschaftliches Rechnen. Gwdg Bericht 63. Ges. Fr Wiss, pp. 79{93. Datenverarbeitung, Gttingen.

Harless, D. W. and C. F. Camerer (1994). The predictive utility of generalized expected utility theories. Econometrica 62 (6), 1251-1289. 22

Harrison, G. W., E. Johnson, M. M. McInnes, and E. E. Rutstrm (2005). Risk aversion and incentive eects: Comment. The American Economic Review 95 (3), 897-901.

Harrison, G. W., M. I. Lau, and E. E. Rutstrom (2009). Risk attitudes, randomization to treatment, and self-selection into experiments. Journal of Economic Behavior & Organization 70 (3), 498-507.

Harrison, G. W., M. I. Lau, and E. E. Rutstrom (2012). Theory, experimental design and econometrics are complementary. In G. Frechette and A. Schotter (Eds.), Methods of modern experimental economics, pp. (forthcoming). Oxford, UK: Oxford University Press.

Harrison, G. W. and E. E. Rutstrom (2009). Expected utility theory and prospect theory: One wedding and a decent funeral. Experimental Economics 12 (2), 133-158.

Hey, J. (2005). Why we should not be silent about noise. Experimental Economics 8 (4), 325-345.

Hey, J. D. and C. Orme (1994). Investigating generalizations of expected utility theory using experimental data. Econometrica 62 (6), 1291-1326.

Holt, C. A. and S. K. Laury (2002). Risk aversion and incentive eects. The American Economic Review 92 (5), 1644-1655.

Loomes, G. (2005). Modelling the stochastic component of behaviour in experiments: Some issues for the interpretation of data. Experimental Economics 8 (4), 301-323.

Luce, R. D. (1959). Individual Choice Behavior: A Theoretical Analysis. New York: Wiley.

McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in Econometrics, pp. 105{142. New York: Academic Press.

Norwood, B. F., J. L. Lusk, and B. W. Brorsen (2004). Model selection for discrete dependent variables: Better statistics for better steaks. Journal of Agricultural and Resource Economics 29 (3), 404-419.

Norwood, B. F., M. C. Roberts, and J. L. Lusk (2004). Ranking crop yield models using out-of-sample likelihood functions. American Journal of Agricultural Economics 86 (4), 1032-1043.

Quiggin, J. (1982). A theory of anticipated utility. Journal of Economic Behavior & Organization 3 (4), 323-343.

Roy, R., P. K. Chintagunta, and S. Haldar (1996). A framework for investigating habits, "the hand of the past," and heterogeneity in dynamic brand choice. Marketing Science 15 (3),280-299.

Tversky, A. and D. Kahneman (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty 5 (4), 297-323.

Vuong, Q. H. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica 57 (2), 307-333.

Wilcox, N. T. (2011). `Stochastically more risk averse:' a contextual theory of stochastic discrete choice under risk. Journal of Econometrics 162 (1), 89-104.