This dissertation consists of three chapters, each of which proposes methods to deal with the "many moments" problem in a different model. Chapter I develops shrinkage methods for solving the "many moments" problem in the context of instrumental variable estimation. The procedure can be...

In this paper, we propose a doubly robust method to present the heterogeneity of the average treatment effect with respect to observed covariates of interest. We consider a situation where a large number of covariates are needed for identifying the average treatment effect but the covariates of...

This paper proposes the analysis of panel data whose dynamic structure is heterogeneous across individuals. Our aim is to estimate the cross-sectional distributions and/or some distributional features of the heterogeneous mean and autocovariances. We do not assume any specific model for the...

This paper studies the asymptotic efficiency in factor models with serially correlated errors and dynamic panel data models with interactive effects. We derive the efficiency bound for the estimation of factors, factor loadings and common parameters that describe the dynamic structure. We use...

In this paper we consider the estimation of a dynamic panel autoregressive (AR) process of possibly innite order in the presence of individual effects. We utilize the sieve AR approximation with its lag order increasing with the sample size. We establish the consistency and asymptotic normality...

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...

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 proposes asymptotically unbiased estimators of autocovariances and autocorrelations for panel data with both individual and time effects. We show that the conventional autocovariance estimators suffers from the bias caused by the elimination of individual and time effects. The bias...