Abstract
This note discusses the pros and cons of using the conditional mean approach of Mundlak and Chamberlain and the linear difference approach to deal with the incidental parameters issue in estimating fixed effects dynamic panel data models. The importance of the data generating process of the explanatory variables and the proper treatment of initial values for either approach to get asymptotically unbiased estimators are demonstrated both analytically and through Monte Carlo studies.
Acknowledgments
The author also wishes to thank two referees for helpful comments and Yimeng Xie for computational assistance.
Notes
1 Monte Carlo studies conducted by Hsiao and Zhou (Citation2018) show that, although, using the ith individual’s time series mean, is more restrictive than using using in lieu of tends to perform better if N and T are of the same magnitude.
2 The estimator Equation(3.7)(3.7) (3.7) is identical to the Bai (Citation2013) factor estimator.
3 Equation Equation(4.3)(4.3) (4.3) is derived by backcasting if is stationary. As argued in Remark 3.1, since is time varying, it is expected that using or performs better than using We prefer to use because it is expected that provides little predictive power for .
4 If there is a trend for xit, Equation(4.14)(4.14) (4.14) restricts the trend coefficient to be identical over i.