Linear regression for panel with unknown number of factors as interactive fixed effects - Hyungsik Roger Moon; Martin Weidner (Centre for Microdata Methods and Practice (cemmap), 2014)
In this paper we study the least squares (LS) estimator in a linear panel regression model with unknown number of factors appearing as interactive fixed effects. Assuming that the number of factors used in estimation is larger than the true number of factors in the data we establish the limiting distribution of the LS estimator for the regression coefficients, as the number of time periods and the number of crosssectional units jointly go to infinity. The main result of the paper is that under certain assumptions the limiting distribution of the LS estimator is independent of the number of factors used in the estimation, as long as this number is not underestimated. The important practical implication of this result is that for inference on the regression coefficients one does not necessarily need to estimate the number of interactive fixed effects consistently.
Keywords: C23; C33; 330; Panel data; interactive fixed effects; factor models; perturbation theory of linear operators; random matrix theory