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Superioridad relativa de los estimadores Kiviet y Blundell-Bond (GMM1) en paneles dinámicos. Un experimento Monte Carlo con muestras finitas
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Superioridad relativa de los estimadores Kiviet y Blundell-Bond (GMM1) en paneles dinámicos. Un experimento Monte Carlo con muestras finitas
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Superioridad relativa de los estimadores Kiviet y Blundell-Bond (GMM1) en paneles dinámicos. Un experimento Monte Carlo con muestras finitas
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Journal Article
Superioridad relativa de los estimadores Kiviet y Blundell-Bond (GMM1) en paneles dinámicos. Un experimento Monte Carlo con muestras finitas
2012
Overview
Dado el amplio uso de los datos de panel en modelos dinamicos, es relevante evaluar el desempeno de sus diferentes estimadores en muestras finitas en presencia de baja y alta persistencia. El presente articulo tiene como objetivo analizar, mediante simulaciones tipo Monte Carlo, las propiedades de los estimadores de efectos fijos (LSDV), Arellano y Bond (AB-GMM1), Blundell y Bond (BB-GMM1), Anderson y Hsiao (AH) y Kiviet. Se concluye que en series no persistentes el estimador de Kiviet es el de mejor desempeno, basandose en los criterios de error cuadratico medio, sesgo y desviacion estandar; con alta persistencia, el estimador BB-GMM1 es el de mejor desempeno seguido por el estimador de Kiviet, que se comporta bien excepto en micropaneles con series persistentes.
Given the widespread use of panel data in dynamic models, it is worth evaluating the performance of different estimators in finite samples in the presence of low and high persistence, with the latter being present in many macroeconomic series. This article analyzes the properties of the Least Square Dummy Variable (LSDV) estimators, Arellano-Bond Generalized Method of Moments Stage 1 (AB-GMM1), BBGMM1 (), AH (Anderson-Hsiao), and Kiviet using a Monte Carlo experiment. The results show that, in the presence of low persistence, the Kiviet estimator is the best performer based on the criteria of Root-Mean-Square Error (RMSE), bias and standard deviation. Meanwhile in the case of high persistence, the system estimator of Blundell and Bond (GMM1) is the best performing estimator against their rivals, followed by Kiviet estimator that exhibits good behavior, except in micropanels.
Devido a ampla utilizacao dos dados do painel em modelos dinamicos, e relevante avaliar o desempenho dos seus diferentes avaliadores em amostras finitas na presenca de baixa e alta persistencia. O presente artigo tem como objectivo analisar, atraves de simulacoes tipo Monte Carlo, as propriedades dos avaliadores de Efeitos Fixos (LSDV), Arrellano e Bond (AB-GMM1), Blundell e Bond (BB-GMM1), Anderson e Hsiao (AH) e Kiviet. Conclui-se que em series nao persistentes o avaliador de Kiviet e o de melhor desempenho baseando-se nos criterios de erro quadratico medio, obliquidade e desvio padrao; com alta persistencia o avaliador BB-GMM1 e o melhor desempenho seguido pelo avaliador de Kiviet que se comporta bem excepto em micro-paineis com series persistentes.
Publisher
Elsevier España,Universidad ICESI,Universidad Icesi
