Abstract. Recent developments in the theory of choice under uncertainty and risk yield a pessimistic decision theory that replaces the classical expected utility criterion with a Choquet expectation that accentuates the likelihood of the least favorable outcomes. A parallel theory has recently emerged in the literature on risk assessment. It is shown that pessimistic portfolio optimization based on the Choquet approach may be formulated as an exercise in quantile regression.
SUMMARYThis paper extends results regarding smoothed median binary regression to general smoothed binary quantile regression, discusses the interpretation of the resulting estimators under alternative assumptions, and shows how they may be used to obtain semiparametric estimates of counterfactual probabilities. The estimators are applied to a model of labour force participation of married women in the USA. We find that the elasticity with respect to non-labour income is significantly negative only for women that belong to the middle of the conditional willingness-to-participate (WTP) distribution. In comparing the quantile models with parametric logit and semiparametric single-index specifications, we find that the models agree closely for women around the centre of the WTP distribution, but there are considerable disagreements as we move towards the tails of the distribution.
Abstract. Recent developments in the theory of choice under uncertainty and risk yield a pessimistic decision theory that replaces the classical expected utility criterion with a Choquet expectation that accentuates the likelihood of the least favorable outcomes. A parallel theory has recently emerged in the literature on risk assessment. It is shown that pessimistic portfolio optimization based on the Choquet approach may be formulated as an exercise in quantile regression.
This paper considers microeconometric evaluation by matching methods when selection in to the program under consideration is heterogeneous. Existing studies generally use parametric estimators of binary response models such as the probit and logit to estimate the propensity score, which allows for very limited forms of heterogeteity and imposes strong distributional assumptions on the error term that are often violated with the underlying data. We introduce an easy to implement matching strategy that incorporates semiparametric propensity scores that allow for very general forms of heterogeneity in response across observed covariates along the conditional willingness to participate in the treatment intervention distribution. Data from the NSW experiment, CPS and PSID are used to evaluate the performance of alternative matching estimators. We find significant evidence of heterogeneity and that the proposed algorithm generally exhibits lower bias and accurately captures the experimental treatment impact. A detailed examination of the average absolute bias errors between our procedure and matching algorithms based on parametric propensity scores indicate reductions between 6.2% and 706% of the experimental program impact.
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