We consider censored structural latent variables models where some exogenous variables are subject to additive measurement errors. We demonstrate that overidentification conditions can be exploited to provide natural instruments for the variables measured with errors, and we propose a two-stage...

This paper deals with censored or truncated regression models where the explanatory variables are measured with additive errors. We propose a two-stage estimation procedure that combines the instrumental variable method and the minimum distance estimation. This approach produces consistent and...

This paper deals with a nonlinear errors-in-variables model where the distributions of the unobserved predictor variables and of the measurement errors are nonparametric. Using the instrumental variable approach, we propose method of moments estimators for the unknown parameters and...

An efficient Monte Carlo method for random sample generation from high dimensional distributions of complex structures is developed. The method is based on random discretization of the sample space and direct inversion of the discretized cumulative distribution function. It requires only the...

Based on the Smile Curve and Michael E. Porter’ value chain model, this paper points out that China’s forestry industry stays at the low end of the value chain for a long time. Raw materials are imported from foreign countries and also sold in foreign countries. Then, the authors...

When analyzing treatment effects, the average treatment value is frequently compared to that of the control group. This approach, naturally, is not particularly informative about specifc regions of the treatment and control distributions. For this reason and having in view a specifc application,...

This paper proposes a simple method to adjust for measurement errors in estimations of many popular limited dependent variable models, e.g., the binary response model, the censored and the truncated regression models. The procedure is based on a simple correction of the estimators for the...