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A Parsimonious Choquet Model of Subjective Life Expectancy

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  • Alexander Ludwig
  • Alexander Zimper

Abstract

This paper develops and estimates a closed-form model of Bayesian learning of subjective survival beliefs within the framework of Choquet decision theory. Data from the Health and Retirement Study (HRS) indicate that, on average, young respondents underestimate their true survival probability whereas old respondents overestimate their survival probability. Such subjective beliefs violate the rational expectations paradigm and are also not in line with the predictions of the rational Bayesian learning paradigm which implies convergence of subjective to underlying 'objective' probabilities. Based on the assumption of non-additive beliefs, we therefore introduce a model of Bayesian learning which combines rational learning with the possibility that the interpretation of new information is prone to psychological attitudes. We estimate the parameters of our theoretical model by pooling the HRS data. Despite a parsimonious parametrization we find that our Choquet model results in a remarkable fit to the average subjective beliefs expressed in the data.

Suggested Citation

  • Alexander Ludwig & Alexander Zimper, 2008. "A Parsimonious Choquet Model of Subjective Life Expectancy," Working Papers, Center for Retirement Research at Boston College wp2008-20, Center for Retirement Research, revised Dec 2008.
  • Handle: RePEc:crr:crrwps:wp2008-20
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    File URL: http://crr.bc.edu/working-papers/a-parsimonious-choquet-model-of-subjective-life-expectancy/
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    Cited by:

    1. Franco Peracchi & Valeria Perotti, 2010. "Subjective survival probabilities and life tables: Evidence from Europe," EIEF Working Papers Series 1016, Einaudi Institute for Economics and Finance (EIEF), revised Nov 2011.
    2. Alexander Zimper & Alexander Ludwig, 2009. "On attitude polarization under Bayesian learning with non-additive beliefs," Journal of Risk and Uncertainty, Springer, vol. 39(2), pages 181-212, October.
    3. Alexander Zimper, 2011. "Do Bayesians Learn Their Way Out of Ambiguity?," Decision Analysis, INFORMS, vol. 8(4), pages 269-285, December.

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