Parametric preference functionals under risk in the gain domain: A Bayesian analysis
Tóm tắt
The performance of rank dependent preference functionals under risk is comprehensively evaluated using Bayesian model averaging. Model comparisons are made at three levels of heterogeneity plus three ways of linking deterministic and stochastic models: differences in utilities, differences in certainty equivalents and contextual utility. Overall, the “best model”, which is conditional on the form of heterogeneity, is a form of Rank Dependent Utility or Prospect Theory that captures most behaviour at the representative agent and individual level. However, the curvature of the probability weighting function for many individuals is S-shaped, or ostensibly concave or convex rather than the inverse S-shape commonly employed. Also contextual utility is broadly supported across all levels of heterogeneity. Finally, the Priority Heuristic model is estimated within a stochastic framework, and allowing for endogenous thresholds does improve model performance although it does not compete well with the other specifications considered.
Tài liệu tham khảo
Andersen, S., Harrison, G. W., Lau, M.I., & Rutström, E. (2008). Eliciting risk and time preferences. Econometrica, 76(3), 583–618.
Andersen, S., Harrison, G. W., Lau, M. I., & Rutström, E. (2010). Behavioral econometrics for psychologists. Journal of Economic Psychology, 31, 553–576.
Andrews, D.K. (1998). Hypothesis Testing with a restricted parameter space. Journal of Econometrics, 84, 155–199.
Andrieu, C., & Thoms, J. (2008). A tutorial on adaptive MCMC. Statistical Computation, 18, 343–373.
Barberis, N.C. (2013). Thirty years of prospect theory in economics: a review and assessment. Journal of Economic Perspectives, 27(1), 173–196.
Birnbaum, M. H. (2006). Evidence against prospect theories in gambles with positive, negative, and mixed consequences. Journal of Economic Psychology, 27, 737–761.
Birnbaum, M. H. (2008). Evaluation of the priority heuristic as a descriptive model of risky decision making: Comment on Brandstätter, Gigerenzer, Hertwig (2006). Psychological Review, 115(1), 253–260.
Birnbaum, M. H., & Chavez, A. (1997). Tests of theories of decision-making: Violations of branch independence and distribution independence. Organizational Behavior and Human Decision Processes, 71(2), 161–194.
Booij, A.S., van Praag, B.M.S., & van de Kuilen, G. (2010). A parametric analysis of prospect theory’s functionals for the general population. Theory and Decision, 68, 115–148.
Brandstätter, E., Gigerenzer, G., & Hertwig, R. (2006). The priority heuristic: Making choices without trade-offs. Psychological Review, 113(2), 409–432.
Brandstätter, E., Gigerenzer, G., & Hertwig, R. (2008). Risky choice with heuristics reply to Birnbaum (2008) Johnson Schulte-Mecklenbeck and Willemsen (2008) and Rieger and Wang (2008). Psychological Review, 115(1), 281–290.
Bruhin, A., Fehr-Duda, H., & Epper, T. (2010). Risk and rationality: uncovering heterogeneity in probability distortion. Econometrica, 78(4), 1375–1412.
Conte, A., Hey, J.D., & Moffat, J. (2011). Mixture models of choice under risk. Journal of Econometrics, 162, 79–88.
Cox, J.C., & Sadiraj, V. (2006). Small- and large-stakes risk aversion: implications of concavity calibration for decision theory. Games and Economic Behaviour, 56, 45–60.
Cox, J.C., Sadiraj, V., Vogt, B., & Dasgupta, U. (2013). Is there a plausible theory for decision under risk? A Dual Calibration Critique. Economic Theory, 53 (2), 305–333.
Fiedler, S., & Glöckner, A. (2012). The dynamics of decision making in risky choice: an eye-tracking analysis. Frontiers in Psychology, 3, Article 335.
Fehr-Duda, H., & Epper, T. (2012). Probability and risk: foundations and economic implications of probability-dependent risk preferences. Annual Review of Economics, 4(1), 567–593.
Fernandez, C., Ley, E., & Steel, M.F. (2001). Benchmark priors for Bayesian model averaging. Journal of Econometrics, 100, 381–427.
Findley, D.F. (1990). Making difficult model comparisons, Bureau of the Census Statistical Research Division Report Series, SRD Research Report Number: CENSUS/SRD/RR-90/11.
Gelfand, A., & Dey, D. (1994). Bayesian model choice: asymptotics and exact calculations. Journal of the Royal Statistical Society (Series B), 56, 501–504.
Green, P.J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4), 711–32.
Goldstein, W. M., & Einhom, H. J. (1987). Expression theory and the preference reversal phenomena. Psychological Review, 94(2), 236–254.
Harless, D.W., & Camerer, C.F. (1994). The predictive utility of generalized expected utility theories. Econometrica, 62(6), 1251–1289.
Harrison, G.W., Humphrey, S.J., & Verschoor, A. (2010). Choice under uncertainty: evidence from Ethiopia, India and Uganda. Economic Journal, 120, 80–104.
Harrison, G.W., & Rutström, E. (2009). Expected utility and prospect theory: one wedding and a decent funeral. Experimental Economics, 12(2), 133–158.
Hey, J. D., & Orme, C. (1994). Investigating generalizations of expected utility theory using experimental data. Econometrica, 62(6), 1291–1326.
Ingersoll, J. (2008). Non-monotonicity of the Tversky-Kahneman probability-weighting function: a cautionary note. European Financial Management, 14(3), 385–390.
Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47, 263–291.
Koop, G. (2003). Bayesian econometrics. Chichester: Wiley.
Nilsson, H., Rieskampa, J., & Wagenmakers, E.J (2011). Hierarchical Bayesian parameter estimation for cumulative prospect theory. Journal of Mathematical Psychology, 55, 84–93.
Pesaran, M. H., & Weeks, M. (2007). Nonnested hypothesis testing: an overview. In Baltagi, B.H. (Ed.) A Companion to Theoretical Econometrics. Malden, MA, USA: Blackwell Publishing Ltd.
Rabin, M. (2000). Risk aversion and expected-utility theory: a calibration theorem. Econometrica, 68, 1281–1292.
Rabin, M., & Thaler, R. H. (2001). Anomalies risk aversion. Journal of Economic Perspectives, 15(1), 219–232.
Sala-i-Martin, X., Doppelhofer, G., & Miller, R. (2004). Determinants of long-term growth: a Bayesian averaging of classical estimates (BACE) approach. American Economic Review, 94, 813–835.
Shleifer, A. (2012). Psychologists at the gate: a review of Daniel Kahneman’s Thinking, Fast and Slow. Journal of Economic Literature, 50(4), 1–12.
Stott, H.P. (2006). Cumulative prospect theory’s functional menagerie. Journal of Risk and Uncertainty, 32, 101–130.
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: cumulative representations of uncertainty. Journal of Risk and Uncertainty, 5, 297–323.
Vuong, Q.H. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica, 57(2), 307–333.
Wakker, P.P. (2010). Prospect theory: for risk and ambiguity. Cambridge: Cambridge University Press.
Wilcox, N.T. (2011). Stochastically more risk averse: A contextual theory of stochastic discrete choice under risk. Journal of Econometrics, 162, 89–104.