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 online appendix accompanies the paper "Misclassification Errors and the Underestimation of the U.S. Unemployment Rate" by Shuaizhang Feng and Yingyao Hu. Section 1 of the appendix lists summary statistics of the CPS sample used in the paper. Section 2 of the appendix provides a detailed...

Using recent results in the measurement error literature, we show that the official U.S. unemployment rate substantially underestimates the true level of unemployment, due to misclassification errors in the labor force status in the Current Population Survey. During the period from January 1996...

This paper provides sufficient conditions for the nonparametric identification of the regression function m(.) in a regression model with an endogenous regressor x and an instrumental variable z. It has been shown that the identification of the regression function from the conditional...

This paper proposes a new semi-nonparametric maximum likelihood estimation method for estimating production functions. The method extends the literature on structural estimation of production functions, started by the seminal work of Olley and Pakes (1996), by relaxing the scalar-unobservable...

This paper proposes a new semi-nonparametric maximum likelihood estimation method for estimating production functions. The method extends the literature on structural estimation of production functions, started by the seminal work of Olley and Pakes (1996), by relaxing the scalar-unobservable...