The Effect of Serial Correlation in Estimating Dynamic Panel Data Models

Abstract

There are several methods of estimating dynamic panel data models in the context of both micro-economic and macro-economic data.  This paper investigates the performance of five different estimators of dynamic panel data models (the random effect model) when the disturbance term is serially correlated.  A  Monte Carlo experiment was conducted when individual, N is large and time dimension, T is finite and the error component model is assumed to be serially correlated. The bias and Root Mean Square Error criterion were used to access the performance of different estimators under consideration. We found that the Anderson-Hsiao using lagged differences as instrument (AH(d)) performs better when the time dimension is small (T=5), Anderson-Hsiao using lagged levels as instrument (AH(l)) performs better when T is moderate(T=10) and the first step Arellano-Bond estimator (ABGMM1) outperforms all other estimators when T increases to 20. For a dynamic panel data with large time dimension, we suggest that the first step Arellano-Bond Estimator (ABGMM1) Estimator is appropriate.  The result shows that the bias of the first step Arellano-Bond estimator (ABGMM1) estimate is severe with small time dimension and the ordinary Least Square (OLS) and Least Square Dummy Variable (LSDV) are also bias when T is small.  It was discovered that the effect of serial correlation is negligible irrespective of the order.