Linear Regression for Panel with Unknown Number of Factors as Interactive Fixed Effects

Supplementary Material Linear Regression for Panel with Unknown Number of Factors as Interactive Fixed Effects

USC Dornsife Institute for New Economic Thinking Working Paper No. 15-03 Linear Regression for Panel with Unknown Number of Factors as Interactive Fixed Effects

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 ...

Dynamic Linear Panel Regression Models with Interactive Fixed Effects

In the last step we used Assumption ID(ii). Because E [ Tr (e′e) ∣∣∣λ0, f , w] is independent of β, λ, f , we find minimizing Q(β, λ, f) is equivalent to minimizing Q∗(β, λ, f). We decompose ‡Department of Economics and USC Dornsife INET, University of Southern California, Los Angeles, CA 90089-0253. Email: [email protected]. Department of Economics, Yonsei University, Seoul, Korea. §Department of ...

Quantile Regression for Dynamic Panel Data with Fixed Effects

This paper studies estimation and inference in a quantile regression dynamic panel model with fixed effects. Panel data fixed effects estimators are typically biased in the presence of lagged dependent variables as regressors. To reduce the dynamic bias in the quantile regression fixed effects estimator I suggest the use of the instrumental variables quantile regression method of Chernozhukov a...

Linear Regression for Dependently Censored Panel Duration Models with Nonadditive Fixed Effects

This paper proposes estimators for a class of panel duration models with induced dependent censoring and fixed effects which may appear nonadditively in the model. No parametric assumptions are made about the distribution of the random error. The dependent censoring implies that the usual differencing approach common in fixed-effects models will lead to disproportionate weighting of the errors,...