A Unified Approach to Efficient Estimation of Short Linear Panel Regression Models


Speaker:Kazuhiko Hayakawa, Hiroshima University
Date: Friday 25 October 2019
Time: 14.45-15.30
Location: 0.28 Streatham Court

Further details

In this paper, we propose a new approach to estimate short panel regression models. The model considered is very general: the model can be either static or dynamic, the time-varying covariates can include endogenous, predetermined and/or strictly exogenous variables, and time invariant covariates can also be included. The errors can contain standard fixed effect and/or interactive fixed effects. We propose the ML and MD estimators to estimate these models in a unified way. Monte Carlo simulation results reveals that the proposed ML and MD estimators outperform most of the existing estimators such as GMM estimators.