Estimating dynamic Panel data. A practical approach to perform long panels

• Autores: Romilio Labra, Celia Torrecillas Bautista
• Localización: Revista Colombiana de Estadística, ISSN-e 2389-8976, ISSN 0120-1751, Vol. 41, Nº. 1, 2018, págs. 31-52
• Idioma: inglés
• DOI: 10.15446/rce.v41n1.61885
• Títulos paralelos:
  • Estimando Datos de panel dinámicos. Un enfoque práctico para abordar paneles largos
• Enlaces
  • Texto completo
• Resumen
  • español

    Resumen La metodología de Datos de Panel es una de las técnicas más usadas para realizar análisis cuantitativos en el ámbito de las ciencias sociales, especialmente en temas relacionados con la economía y los negocios. Su riqueza reside en que esta técnica permite trabajar con varios periodos de tiempo, incorporar los efectos individuales, y a su vez, tratar la endogeneidad. A pesar de estas ventajas, existen diversos obstáculos para su implementación, tanto metodológicos como operativos. Dentro de los tipos de modelos que se pueden estimar con Datos de Panel, los de carácter estáticos han sido los más desarrollados, persistiendo aún carencias teórico-prácticas para los modelos dinámicos. Este artículo pone precisamente su énfasis en estos últimos, aplicando un enfoque que conjuga la teoría y la praxis, y prestando especial atención a su aplicabilidad para datos macroeconómicos, fundamentalmente para paneles que poseen un período de tiempo largo y un número de individuos pequeño.

  • English

    Abstract Panel data methodology is one of the most popular tools for quantitative analysis in the field of social sciences, particularly on topics related to economics and business. This technique allows simultaneously dressing individual effects, numerous periods, and in turn, the endogeneity of the model or independent regressors. Despite these advantages, there are several methodological and practical limitations to perform estimations using this tool. There are two types of models that can be estimated with Panel data: Static and Dynamic, the former is the most developed while dynamic models still have some theoretical and practical constraints. This paper focuses precisely on the latter, Dynamic panel data, using an approach that combines theory and praxis, and paying special attention on its applicability on macroeonomic data, specially datasets with a long period of time and a small number of individuals, also called long panels.

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