Bias Correction Methods for Dynamic Panel Data Models with Fixed Effects
Mohamed Abonazel ()
MPRA Paper from University Library of Munich, Germany
This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross sectional dimension (N). Although consistent estimates can be obtained by GMM procedures, the inconsistent LS estimator has a relatively low variance and hence can lead to an estimator with lower root mean square error after the bias is removed. Therefore, we discuss in this paper the different methods to correct the bias of LS and GMM estimations. The analytical expressions for the asymptotic biases of the LS and GMM estimators have been presented for large N and finite T. Finally, we display new estimators that presented by Youssef and Abonazel (2015) as more efficient estimators than the conventional estimators.
Keywords: Bias-corrected estimators; First-order autoregressive panel model; Generalized method of moments estimators; Kantorovich inequality; Least squares dummy variable estimators. (search for similar items in EconPapers)
JEL-codes: C01 C1 C23 C4 C5 C87 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
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Working Paper: Bias Correction Methods for Dynamic Panel Data Models with Fixed Effects (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:70628
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