This paper proposes a bootstrap-based procedure to build confidence intervals for single components of a partially identified parameter vector, and for smooth functions of such components, in moment (in)equality models. The extreme points of our confidence interval are obtained by...

This paper proposes an econometric framework to estimate market risk prices associated with risk-neutral measures Q under incomplete markets. We show that, under incomplete markets, the market price of risk is not point-identified but is instead identified as a bounded subset of an affine...

This paper proposes a bootstrap-based procedure to build confidence intervals for single components of a partially identified parameter vector, and for smooth functions of such components, in moment (in)equality models. The extreme points of our confidence interval are obtained by...

This paper studies nonparametric identification in market level demand models for differentiated products with heterogeneous consumers. We consider a general class of models that allows for the individual specific coefficients to vary continuously across the population and give conditions...

This paper studies nonparametric identification in market level demand models for differentiated products with heterogeneous consumers. We consider a general class of models that allows for the individual specific coefficients to vary continuously across the population and give conditions under...

The instrumental variable quantile regression (IVQR) model (Chernozhukov and Hansen, 2005) is a popular tool for estimating causal quantile effects with endogenous covariates. However, estimation is complicated by the non-smoothness and non-convexity of the IVQR GMM objective function. This...

The instrumental variable quantile regression (IVQR) model of Chernozhukov and Hansen (2005, 2006) is a flexible and powerful tool for evaluating the impact of endogenous covariates on the whole distribution of the outcome of interest. Estimation, however, is computationally burdensome because...