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Cross-sectional maximum likelihood and bias-corrected pooled least squares estimators for dynamic panels with short T

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  • In Choi

    (School of Economics, Sogang University, Seoul)

Abstract

This paper proposes new estimators for the panel autoregressive (PAR) models with short time dimensions (T) and large cross sections (N). These estimators are based on the cross-sectional regression model using the first time series observations as a regressor and the last as a dependent variable. The regressors and errors of this regression model are correlated. The first estimator is the maximum likelihood estimator (MLE) under the assumption of normal distributions. This estimator is called the cross-sectional MLE (CSMLE). The second estimator is the bias-corrected pooled least squares estimator (BCPLSE) that eliminates the asymptotic bias of PLSE by using the CSMLE. The CSMLE and BCPLSE are extended to the PAR model with endogenous time-variant and time-invariant regressors. The CSMLE and BCPLSE provide consistent estimates of the PAR coefficients for stationary, unit root and explosive PAR models, estimate the coefficients of time-invariant regressors consistently and can be computed as long as T>=2. Their finite sample properties are compared with those of some other estimators for the PAR model of order 1. The estimators of this paper are shown to perform quite well in finite samples.

Suggested Citation

  • In Choi, 2016. "Cross-sectional maximum likelihood and bias-corrected pooled least squares estimators for dynamic panels with short T," Working Papers 1610, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    • Handle: RePEc:sgo:wpaper:1610
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    References listed on IDEAS

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    Keywords

    dynamic panels; maximum likelihood estimator; pooled least squares estimator; stationarity; unit root; explosiveness;
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