We study Ackerberg, Caves, and Frazer (2015)'s production function estimation method using Monte Carlos simulations. First, we replicate their results by using the same procedure and confirm the existence, as noted by ACF, of a spurious minimum in the estimation. In the population, or when the...

Productivity studies that use proxy variable estimators routinely specify single-output production functions despite most firms producing multiple outputs. This is usually accomplished by aggregating the firm's outputs using total revenue. Such a formulation rarely provides an adequate...

Missing values are a major problem in all econometric applications based on survey data. A standard approach assumes data are missing-at-random and uses imputation methods, or even listwise deletion. This approach is justified if item non-response does not depend on the potentially missing...

We give a general construction of debiased/locally robust/orthogonal (LR) moment functions for GMM, where the derivative with respect to first step nonparametric estimation is zero and equivalently first step estimation has no effect on the influence function. This construction consists of...

Let H 0 (X) be a function that can be nonparametrically estimated. Suppose E [ Y | X ]= F 0 [ X ß 0 H 0 (X) ] . Many models fit this framework, including latent in- dex models with an endogenous regressor and nonlinear models with sample se- lection. We show that the vector ß 0 and unknown...

This paper demonstrates how Goal Programming/Constrained Regression can be used for cross-checking results from standard econometric models as well as a stand alone methodology in empirical production analysis. For illustration, we re-examine Berndt and Wood's (BW) seminal study of the U.S....

This paper aims to provide empirical researchers with an overview of the methodological issues that arise when estimating total factor productivity at the establishment level, as well as of the existing (parametric and semiparametric) techniques designed to overcome them. Apart from the...

This paper has presented a nonparametric approach to estimate the deterministic frontier model with unknown function form. Single index modeling and partially linear modeling techniques have been modified for estimation of a semi-parametric deterministic frontier model. Those approaches are free...

We consider a difference based ridge regression estimator and a Liu type estimator of the regression parameters in the partial linear semiparametric regression model, y = Xβ + f + Both estimators are analysed and compared in the sense of mean-squared error. We consider the case of independent...