We develop a practical way of addressing the Errors-In-Variables (EIV) problem in the Generalized Method of Moments (GMM) framework. We focus on the settings in which the variability of the EIV is a fraction of that of the mismeasured variables, which is typical for empirical applications. For...

Virtually all methods aimed at correcting for covariate measurement error in regressions rely on some form of additional information (e.g., validation data, known error distributions, repeated measurements or instruments). In contrast, we establish that the fully nonparametric classical...

This paper estimates a structural model of private provision of public goods to provide some new empirical evidence on individuals' strategic contributing behaviors. In the model, individuals' contributing behaviors are allowed to be heterogenous and time-varying. We show that all the main...

This paper develops the identification and estimation of nonlinear semi-parametric panel data models with mismeasured variables and their corresponding average partial effects using only three periods of data. The past observables are used as instruments to control the measurement error problem,...

Berkson errors are commonplace in empirical microeconomics and occur whenever we observe an average in a specified group rather than the true individual value. In consumer demand this form of measurement error is present because the price an individual pays is often measured by the average price...

This paper provides a test for completeness in a class of nonparametric specification with an additive and independent error term. It is known that such a nonparametric location family of functions is complete if and only if the characteristic function of the error term has no zeros on the real...

This paper presents a novel self-report approach to identify a general causal model with an unobserved covariate, which can be unobserved heterogeneity or an unobserved choice variable. It shows that a carefully designed noninvasive survey procedure can provide enough information to identify the...

This paper considers nonparametric identification and estimation of the regression function when a covariate is mismeasured. The measurement error need not be classical. Employing the small measurement error approximation, we establish nonparametric identification under weak and...