Estimators that exploit an instrumental variable to correct for misclassification in a binary regressor typically assume that the misclassification rates are invariant across all values of the instrument. We show that this assumption is invalid in routine empirical settings. We derive a new...

We compile data for 186 countries (1919 - 2016) and apply different aggregation methods to create new democracy indices. We observe that most of the available aggregation techniques produce indices that are often too favorable for autocratic regimes and too unfavorable for democratic regimes....

This paper studies the identifying power of an instrumental variable in the nonparametric heterogeneous treatment effect framework when a binary treatment is mismeasured and endogenous. Using a binary instrumental variable, I characterize the sharp identified set for the local average treatment...

We consider nonlinear parametric models with an independent variable that is measured with error. The measurement error can be correlated with the true value, i.e., the measurement error is allowed to be nonclassical. We propose a control variable estimator for the parameters of interest. The...

Measurement error causes a downward bias when estimating a panel data linear regression model. The panel data context offers various opportunities to derive moment conditions that result in consistent GMM estimators. We consider three sources of moment conditions: (i) restrictions on the...

This chapter discusses how applied researchers in corporate finance can address endogeneity concerns. We begin by reviewing the sources of endogeneity—omitted variables, simultaneity, and measurement error—and their implications for inference. We then discuss in detail a number of...

Although proxy variables are pervasive in empirical work, the quality of proxy variables---in terms of how closely they track underlying economic forces---is not known. We derive novel regression specifications to infer the severity of measurement error using a sample of 2,552 instrumental...

The rich dependency structure of panel data can be exploited to generate moment conditions that can be used to identify linear regression models in the presence of measurement error. This paper adds to a small body of literature on this topic by showing how heteroskedasticity and nonlinear...

Measurement error causes a downward bias when estimating a panel data linear regression model. The panel data context offers various opportunities to derive moment conditions that result in consistent GMM estimators. We consider three sources of moment conditions: (i) restrictions on the...

This paper reviews recent developments in nonparametric identification of measurement error models and their applications in applied microeconomics, in particular, in empirical industrial organization and labor economics. Measurement error models describe mappings from a latent distribution to...