In this paper we consider the estimation of a dynamic panel autoregressive (AR) process of possibly infinite order in the presence of individual effects. We employ double asymptotics under which both the cross-sectional sample size and the length of time series tend to infinity and utilize the...

This paper demonstrates the importance of simultaneously considering two behavioral biases, correlation neglect and overprecision, in characterizing belief formation. Our laboratory experiments reveal that, relative to independent signals, subjects overvalue moderately or strongly correlated...

This paper proposes a new model-averaging method, called the Heteroskedasticity-Robust Cp (HRCp) method, for linear regression models with heteroskedastic errors. We provide a feasible form of the Mallows' Cp-like criterion for choosing the weighting vector for averaging. Under some regularity...

This online appendix includes supplemental materials for "Network-motivated Lending Decisions: A Rationale for Forbearance Lending'' by Ogura, Okui, and Saito. Online Appendix 1 contains the proofs of the propositions. Online Appendix 2 presents a numerical example for the theoretical model....

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 propose a method of averaging generalized least squares estimators for linear regression models with heteroskedastic errors. The averaging weights are chosen to minimize Mallows' Cp-like criterion. We show that the weight vector selected by our method is optimal. It is also...

This paper considers linear panel data models with a grouped pattern of heterogeneity when the latent group membership structure and/or the values of slope coefficients change at a break point. We propose a least squares approach to jointly estimate the break point, group membership structure,...