Juodis, A. and Sarafidis, V. (2022). An incidental parameters free inference approach for panels with common shocks Journal of Econometrics, 229(1):19--54.

This paper develops a novel Method of Moments approach for panel data models with endogenous regressors and unobserved common factors. The proposed approach does not require estimating explicitly a large number of parameters in either time-series or cross-sectional dimension, T and N respectively. Hence, it is free from the incidental parameter problem. In particular, the proposed approach does not suffer from {\textquotedblleft}Nickell bias{\textquotedblright} of order O(T−1), nor from bias terms that are of order O(N−1). Therefore, it can operate under substantially weaker restrictions compared to existing large T procedures. Two alternative GMM estimators are analyzed; one makes use of a fixed number of {\textquotedblleft}averaged estimating equations{\textquotedblright} à la Anderson and Hsiao (1982), whereas the other one makes use of {\textquotedblleft}stacked estimating equations{\textquotedblright}, the total number of which increases at the rate of O(T). It is demonstrated that both estimators are consistent and asymptotically mixed-normal as N→∞ for any value of T. Low-level conditions that ensure local and global identification in this setup are examined using several examples.