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Does the Stochastic Specification Matter?

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  • Konstantina Mari

Abstract

This paper highlights the importance and significance of the stochastic assumptions underlying any empirical analysis. The stochastic assumptions in any data analysis are usually implicit, rather than explicitly, stated. For example, in a binomial option pricing model, we assume that we have a binary random variable. In this paper we examine the significance of the stochastic assumptions by looking at the statistical properties of stated allocations (in an allocation problem) and their relation to the optimal allocation. The message that emerges is an important one: that the stochastic specification underlying any statistical analysis matters for the interpretation of its results. Our results suggest that before doing any statistical analysis one should carry out extensive simulations.

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  • Konstantina Mari, 2017. "Does the Stochastic Specification Matter?," Discussion Papers 17/05, Department of Economics, University of York.
    • Handle: RePEc:yor:yorken:17/05
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    References listed on IDEAS

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    1. John D. Hey & Chris Orme, 2018. "Investigating Generalizations Of Expected Utility Theory Using Experimental Data," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 3, pages 63-98, World Scientific Publishing Co. Pte. Ltd..
    2. Charles A. Holt & Susan K. Laury, 2002. "Risk Aversion and Incentive Effects," American Economic Review, American Economic Association, vol. 92(5), pages 1644-1655, December.
    3. Loomes, Graham, 1991. "Evidence of a New Violation of the Independence Axiom," Journal of Risk and Uncertainty, Springer, vol. 4(1), pages 91-108, January.
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