The estimation and inference for conditional estimating equations models with endogenous selection, are considered. approach takes into account possible selection which may lead to a bias. It can be used wide range of statistical not covered by the model-based sampling theory. Endogeneity either part or within covariates. is particularly well suited unknown heteroscedasticity, uncontrolled conf...

Education is a well-known driver of (entrepreneurial) income. The measurement of its influence, however, suffers from endogeneity suspicion. For instance, ability and occupational choice are mentioned as driving both the level of (entrepreneurial) income and of education. Using instru-mental variables can provide a way out. However, two questions remain: whether endogeneity is really present an...

Stochastic frontier models are typically estimated by maximum likelihood (MLE) orcorrected ordinary least squares. The consistency of either estimator depends on exogeneity of the explanatory variables (inputs, in the production frontier setting). We will investigate the case that one or more of the inputs is endogenous, in the simultaneous equation sense of endogeneity. That is, we worry that ...

The impact of endogeneity and measurement error on the estimated effect of household economic resources on child height is demonstrated. These econometric problems contribute to uncertainty in the literature about the significance of income effects on child health and nutrition. Results are based on a household survey from Papua New Guinea, where repeated within-year observations on households ...

In this paper we propose a method to estimate models in which an endogenous dichotomous treatment affects a count outcome in the presence of either sample selection or endogenous participation using maximum simulated likelihood. We allow for the treatment to have an effect on both the sample selection or the participation rule and the main outcome. Applications of this model are frequent in man...

This paper proposes a new two stage least squares (2SLS) estimator which is consistent and asymptotically normal in the presence of many weak and irrelevant instruments and heteroskedasticity. In the first stage the estimator uses an adaptive absolute shrinkage and selection operator (LASSO) that selects the relevant instruments with high probability. However, the adaptive LASSO estimates have ...