Valentino Dardanoni

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This paper identifies two distinct types of payoff kinks that can be exhibited by preference functions over monetary lotteries – " locally separable " vs. " locally nonseparable " – and illustrates their relationship to the payoff and probability derivatives of such functions. Expected utility and Fréchet differentiable preference functions are found to be(More)
The aim of this paper is to test for stochastic monotonicity in intergenerational socioeconomic mobility tables. In other words we question whether having a parent from a high socioeconomic status is never worse than having one with a lower status. Using existing inferential procedures for testing unconditional stochastic monotonicity, we first test a set(More)
JEL classification: C12 C13 C19 Keywords: Missing covariates Imputations Bias-precision trade-off Model reduction Model averaging BMI and income a b s t r a c t A common problem in applied regression analysis is that covariate values may be missing for some observations but imputed values may be available. This situation generates a trade-off between bias(More)
The Medicare program, which provides insurance coverage to the elderly in the United States, does not protect them fully against high out-of-pocket costs. For this reason private supplementary insurance, named Medigap, has been available to cover Medicare gaps. This paper studies how Medigap affects the utilization of inpatient care, separating the(More)
In this note I consider a simple proof of Arrow's Impossibility Theorem (Arrow 1963). I start with the case of three individuals who have preferences on three alternatives. In this special case there are 133=2197 possible combinations of the three individuals' rational preferences. However, by considering the subset of linear preferences, and employing the(More)
This paper considers estimation of a linear regression model using data where some covariate values are missing but imputations are available to fill-in the missing values. The availability of imputations generates a trade-off between bias and precision in the estimators of the regression parameters: the complete cases are often too few, so precision is(More)