We analyze a class of linear regression models including interactions of endogenous regressors and exogenous covariates. We show that, under typical conditions regarding higher-order dependencies between endogenous and exogenous regressors, the OLS estimator of the coefficient of the interaction...

This paper proposes an ℓ1 penalized quantile regression estimator which adapts the Hausman–Taylor instrumental variable approach in order to address the bias resulting from the shrinkage of the individual effects.

This paper develops shrinkage methods for addressing the “many instruments” problem in the context of instrumental variable estimation. It has been observed that instrumental variable estimators may behave poorly if the number of instruments is large. This problem can be addressed by...

This note discusses partial identification in a nonparametric triangular system with discrete endogenous regressors and nonseparable errors. Recently, Jun et al. (2011, JPX) provide bounds on the structural function evaluated at particular values using exclusion, exogeneity and rank conditions....

The effects of voluntary work on earnings have recently been studied for some developed countries such as Canada, France and Austria. This paper extends this line of research to Italy, using data from the European Union Statistics on Income and Living Conditions (EU-SILC) dataset. A double...

The paper studies the effect of voluntary work on labour income for Italian employees. The Heckman and Instrumental Variables methods are used in order to control for self-selection bias of participation in labour market and endogeneity of volunteering. The results show that a wage premium of 3...

Using a rich Italian cross-sectional data set, we estimate the effect of a neighborhood quality index based on pollution, crime, and noise on self-assessed health, presence of chronic conditions and limitations in daily activities. We address the self-selection of the residents in their...

This paper shows that instrumental variables estimators currently in use, require strong but neglected auxiliary assumptions to be consistent in situations with partially missing instruments. We introduce an alternative instrumental variables estimator that does not require auxiliary assumptions.

We study the scope of local indirect least squares (LILS) methods for nonparametrically estimating average marginal effects of an endogenous cause X on a response Y in triangular structural systems that need not exhibit linearity, separability, or monotonicity in scalar unobservables. One main...

Using a rich Italian cross-sectional dataset, we estimate the effect of a neighborhood problems aggregate (including pollution, crime, and noise) on self-assessed health, presence of chronic conditions and limitations in daily activities. We address the self-selection of the residents in their...