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Journal of Risk and Uncertainty

, Volume 19, Issue 1–3, pp 49–66 | Cite as

Analysis of Choice Expectations in Incomplete Scenarios

  • Charles F. Manski
Article

Abstract

This paper studies the use of probabilistic expectations data to predict behavior in incomplete scenarios posed by the researcher. The information that respondents have when replying to questions posing incomplete scenarios is a subset of the information that they would have in actual choice settings. Hence such questions do not elicit pure statements of preference; they elicit preferences mixed with expectations of future events that may affect choice behavior. The analysis developed here assumes respondents recognize that their behavior may depend on information they do not have when expectations are elicited, and that they answer coherently and honestly given the information provided. The objective in imagining such ideal respondents is to place a logical upper bound on the predictive content of elicited choice expectations.

hypothetical choice intentions revealed preference scenarios 

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References

  1. Afriat, Sidney. (1967). ''The Construction of a Utility Function from Expenditure Data,'' International Economic Review 8, 67–77.Google Scholar
  2. Beggs, S., Scott Cardell, and Jerry Hausman. (1981). ''Assessing the Potential Demand for Electric Cars,'' Journal of Econometrics 16, 1–19.Google Scholar
  3. Ben-Akiva, Moshe and Thawat Morikawa (1990). ''Estimation of Switching Models from Revealed Preferences and Stated Intentions,'' Transportation Research A 24A, 485–495.Google Scholar
  4. Blume, Lawrence, A. Brandenburger, and Edward Dekel. (1991). ''Lexicographic Probabilities and Choice Under Uncertainty,'' Econometrica 59, 61–79.Google Scholar
  5. Dominitz, Jeff and Charles Manski. (1997a). ''Perceptions of Economic Insecurity: Evidence from the Survey of Economic Expectations,'' Public Opinion Quarterly 61, 261–287.Google Scholar
  6. Dominitz, Jeff and Charles Manski. (1997b). ''Using Expectations Data to Study Subjective Income Expectations,'' Journal of the American Statistical Association 92, 855–867.Google Scholar
  7. Dominitz, Jeff and Charles Manski. (1999). ''The Several Cultures of Research on Subjective Expecta-tions.'' In Robert Willis and James Smith eds., Wealth, Work, and Health Ann Arbor, MI: University of Michigan Press.Google Scholar
  8. Fischer, Gregory and Daniel Nagin (1981). ''Random versus Fixed Coefficient Quantal Choice Models.'' In Charles Manski and Daniel McFadden eds., Structural Analysis of Discrete Data with Econometric Applications Cambridge, MA: MIT Press.Google Scholar
  9. Fischhoff, Baruch, Ned Welch, and Shane Frederick. (1999). ''Construal Processes in Preference Assessment,'' Journal of Risk and Uncertainty, 19, 139–164.Google Scholar
  10. Hurd, Michael and Kathleen McGarry. (1995). ''Evaluation of Subjective Probabilities of Mortality in the HRS,'' Journal of Human Resources 30, S268-S292.Google Scholar
  11. Juster, F. Thomas. (1966). ''Consumer Buying Intentions and Purchase Probability: An Experiment in Survey Design,'' Journal of the American Statistical Association 61, 658–696.Google Scholar
  12. Louviere, Jordan and G. Woodworth. (1983). ''Design and Analysis of Simulated Consumer Choice or Allocation Experiments: An Approach Based on Aggregate Data,'' Journal of Marketing Research 20, 350–367.Google Scholar
  13. Luce, R. Duncan. (1959). Individual Choice Behavior: A Theoretical Analysis. New York: Wiley.Google Scholar
  14. Luce, R. Duncan and Patrick Suppes. (1965). ''Preference, Utility, and Subjective Probability.'' In R. Duncan Luce, R. Bush, and E. Galanter eds., Handbook of Mathematical Psychology, Vol. 3. New York: Wiley.Google Scholar
  15. Manski, Charles. (1975). ''Maximum Score Estimation of the Stochastic Utility Model of Choice,'' Journal of Econometrics 3, 205–228.Google Scholar
  16. Manski, Charles. (1985). ''Semiparametric Analysis of Discrete Response: Asymptotic Properties of the Maximum Score Estimator,'' Journal of Econometrics 27, 313–333.Google Scholar
  17. Manski, Charles. (1988). ''Identification of Binary Response Models,'' Journal of the American Statistical Association 83, 729–738.Google Scholar
  18. Manski, Charles. (1990). ''The Use of Intentions Data to Predict Behavior: A Best Case Analysis,'' Journal of the American Statistical Association 85, 934–940.Google Scholar
  19. Manski, Charles. (1995). Identification Problems in the Social Sciences. Cambridge, MA: Harvard University Press.Google Scholar
  20. Manski, Charles. 1997. ''Monotone Treatment Response,'' Econometrica, 65, 1311–1334.Google Scholar
  21. Manski, Charles and Ilan Salomon. (1987). ''The Demand for Teleshopping,'' Regional Science and Urban Economics 17, 109–121.Google Scholar
  22. McFadden, Daniel. (1973). ''Conditional Logit Analysis of Qualitative Choice Behavior.'' In Paul Zarembka ed., Frontiers of Econometrics. New York: Academic Press.Google Scholar
  23. McFadden, Daniel. (1976). ''Quantal Choice Analysis: A Survey,'' Annals of Economic and Social Measurement 5, 363–390.Google Scholar
  24. Quadrel, M., Baruch Fischhoff, and W. Davis. (1993). ''Adolescent In vulnerability,'' American Psychologist 48, 102–116.Google Scholar
  25. Tversky, Amos. (1972). ''Choice-by-Elimination,'' Journal of Mathematical Psychology 9, 341–367.Google Scholar
  26. Varian, Hal. (1982). ''The Nonparametric Approach to Demand Analysis,'' Econometrica 50, 945–973.Google Scholar

Copyright information

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • Charles F. Manski
    • 1
  1. 1.Department of Economics and Institute for Policy ResearchNorthwestern UniversityEvanston

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