EconPapers    
Economics at your fingertips  
 

Alternative GMM Estimators for First-order Autoregressive Panel Model: An Improving Efficiency Approach

Ahmed Youssef and Mohamed Abonazel

MPRA Paper from University Library of Munich, Germany

Abstract: This paper considers first-order autoregressive panel model which is a simple model for dynamic panel data (DPD) models. The generalized method of moments (GMM) gives efficient estimators for these models. This efficiency is affected by the choice of the weighting matrix which has been used in GMM estimation. The non-optimal weighting matrices have been used in the conventional GMM estimators. This led to a loss of efficiency. Therefore, we present new GMM estimators based on optimal or suboptimal weighting matrices. Monte Carlo study indicates that the bias and efficiency of the new estimators are more reliable than the conventional estimators.

Keywords: Dynamic panel data; Generalized method of moments; Kantorovich inequality upper bound; Monte Carlo simulation; Optimal and suboptimal weighting matrices (search for similar items in EconPapers)
JEL-codes: C4 C5 M21 (search for similar items in EconPapers)
Date: 2015-09-28
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/68674/1/MPRA_paper_68674.pdf original version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:68674

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().

 
Page updated 2025-03-19
Handle: RePEc:pra:mprapa:68674