This note considers a puzzling phenomenon that is observed in some semiparametric estimation problems. In some cases, using estimated values of the nuisance parameters provides a more efficient estimator for the parameters of interest than does using the true values. This phenomenon takes place...

An important reason for analyzing panel data is to observe the dynamic nature of an economic variable separately from its time-invariant unobserved heterogeneity. This paper examines how to estimate the autocovariances of a variable separately from its time-invariant unobserved heterogeneity....

This paper considers specification testing for instrumental variables estimation in the presence of many instruments. The test proposed is a modified version of the Sargan (1958, Econometrica 26(3): 393-415) test of overidentifying restrictions. The test statistic asymptotically follows the...

We propose a nonparametric test for Granger-type causality. A conceptually similar work will be Hidalgo (2000), who introduced a nonparametric Granger causality test in the frequency domain for weakly stationary linear processes. He is mainly concerned with the test under long range dependent...

This paper proposes shrinkage methods in instrumental variable estimations to solve the ``many instruments'' problem. Even though using a large number of instruments reduces the asymptotic variances of the estimators, it has been observed both in theoretical works and in practice that in finite...

This short note derives the probability limits of several estimators for panel AR(1) models under misspecification using sequential asymptotics. The results show that GMM estimators based on the forward orthogonal deviation transformation converge to the first-order autocorrelation coefficient.

We consider the estimation of autocovariances using panel data with incidental trends under double asymptotics. The conventional autocovariance estimator suffers from a bias whose value is approximated by twice the long-run variance. We propose a bias-corrected estimator.

We introduce a simple method for constructing a scoring rule to elicit an agent's belief about a random variable that is incentive compatible irrespective of her risk-preference. The agent receives a fixed prize when her prediction error, defined by a loss function specified in the incentive...

This paper proposes a method of averaging generalized least squares (GLS) estimators for linear regression models with heteroskedastic errors. We derive two kinds of Mallows' Cp criteria, calculated from the estimates of the mean of the squared errors of the tted value based on the averaged GLS...

This paper considers model averaging as a way to construct optimal instruments for the two-stage least squares (2SLS), limited information maximum likelihood (LIML), and Fuller estimators in the presence of many instruments. We propose averaging across least squares predictions of the endogenous...