Publication Date: January 2010
This paper introduces a new estimation method for dynamic panel models with ﬁxed eﬀects and AR(p) idiosyncratic errors. The proposed estimator uses a novel form of systematic diﬀerencing, called X-diﬀerencing, that eliminates ﬁxed eﬀects and retains information and signal strength in cases where there is a root at or near unity. The resulting “panel fully aggregated” estimator (PFAE) is obtained by pooled least squares on the system of X-diﬀerenced equations. The method is simple to implement, free from bias for all parameter values, including unit root cases, and has strong asymptotic and ﬁnite sample performance characteristics that dominate other procedures, such as bias corrected least squares, GMM and system GMM methods. The asymptotic theory holds as long as the cross section (n) or time series (T) sample size is large, regardless of the n/T ratio, which makes the approach appealing for practical work. In the time series AR(1) case (n = 1), the FAE estimator has a limit distribution with smaller bias and variance than the maximum likelihood estimator (MLE) when the autoregressive coeﬀicient is at or near unity and the same limit distribution as the MLE in the stationary case, so the advantages of the approach continue to hold for ﬁxed and even small n. For panel data modeling purposes, a general-to-speciﬁc selection rule is suggested for choosing the lag parameter p and the procedure works in a standard manner, aiding practical implementation. The PFAE estimation method is also applicable to dynamic panel models with exogenous regressors. Some simulation results are reported giving comparisons with other dynamic panel estimation methods.
GMM, Panel full aggregation, Stacked and pooled least squares, Panel unit root, X-Diﬀerencing
JEL Classification Codes: C22, C23
See CFP: 1406