In this paper I propose to estimate densities with possibly restricted support semi-nonparametrically (SNP) using SNP densities on the unit interval based on orthonormal Legendre polynomials. This approach will be applied to the interval censored mixed proportional hazard (ICMPH) model, where the distribution of the unobserved heterogeneity is modeled semi-nonparametrically. Various conditions ...

In this paper we introduce a new Poisson mixture model for count panel data where the underlying Poisson process intensity is determined endogenously by consumer latent utility maximization over a set of choice alternatives. This formulation accommodates the choice and count in a single random utility framework with desirable theoretical properties. Individual heterogeneity is introduced throug...

In evaluating credit risk models, it is common to use metrics such as power curves and their associated statistics. However, power curves are not necessarily easily linked intuitively to common lending practices. Bankers often request a specific rule for defining a cut-off above which credit will be granted and below which it will be denied. In this paper we provide some quantitative insight in...

This paper considers information bounds for semiparametric models with endogenous regressors, under different support conditions on the outcome variable as well as the endogenous regressor. Our base case is the model where both the outcome variable and the endogenous regressor are binary. Our first result is that under stated conditions, the information for the coefficient on the endogenous var...

The security of sensitive individual data is a subject of indisputable importance. One of the major threats to sensitive data arises when one can link sensitive information and publicly available data. In this paper we demonstrate that even if the sensitive data are never publicly released, the point estimates from the empirical model estimated from the combined public and sensitive data may le...

Robust Confidence Intervals for Average Treatment Effects under Limited Overlap Estimators of average treatment effects under unconfounded treatment assignment are known to become rather imprecise if there is limited overlap in the covariate distributions between the treatment groups. But such limited overlap can also have a detrimental effect on inference, and lead for example to highly distor...

This paper is concerned with the semiparametric estimation of function means that are scaled by an unknown conditional density function. Parameters of this form arise naturally in the consideration of models where interest is focused on the expected value of an integral of a conditional expectation with respect to a continuously distributed “special regressor” with unbounded support. In particu...

In semiparametric binary response models, support conditions on the regressors are required to guarantee point identification of the parameter of interest. For example, one regressor is usually assumed to have continuous support conditional on the other regressors. In some instances, such conditions have precluded the use of these models; in others, practitioners have failed to consider whether...

In this paper we introduce a new flexible mixed model for multinomial discrete choice where the key individualand alternative-specific parameters of interest are allowed to follow an assumptionfree nonparametric density specification while other alternative-specific coefficients are assumed to be drawn from a multivariate normal distribution which eliminates the independence of irrelevant alter...