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Revista Colombiana de Estadística

Print version ISSN 0120-1751

Rev.Colomb.Estad. vol.41 no.1 Bogotá Jan./June 2018

http://dx.doi.org/10.15446/rce.v41n1.61885 

Artículos originales de investigación

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

Estimando Datos de panel dinámicos. Un enfoque práctico para abordar paneles largos

Romilio Labra1  a  , Celia Torrecillas2  b 

1Instituto de Innovación Basada en Ciencia, Universidad de Talca, Talca, Chile.

2Departamento de Administración y Dirección de Empresas, Facultad de Ciencias Sociales y de la Comunicación, Universidad Europea de Madrid, Madrid, España.

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.

Key words: Dynamic Panels; Endogenous Models; Overidentification; Panel Data; Stata; xtabond2

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.

Palabras-clave: datos de panel; datos de panel dinámicos; modelos endógenos; sobreidentificación; stata

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References

Anderson, T. W. & Hsiao, C. (1981), 'Estimation of dynamic models with error components', Journal of the American statistical Association 76(375), 598-606. [ Links ]

Arellano, M. & Bond, S. (1991), 'Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations', The Review of Economic Studies 58(2), 277-297. [ Links ]

Arellano, M. & Bover, O. (1995), 'Another look at the instrumental variable estimation of error-components models', Journal of Econometrics 68(1), 29-51. [ Links ]

Balestra, P. & Nerlove, M. (1966), 'Pooling cross section and time series data in the estimation of a dynamic model: The demand for natural gas', Econometrica: Journal of the Econometric Society 34(3), 585-612. [ Links ]

Barro, R. (1991), ‘Economic growth in a cross section of countries', Quarterly Journal of Economics (106), 407-443. [ Links ]

Blundell, R. & Bond, S. (1998), 'Initial conditions and moment restrictions in dynamic panel data models', Journal of Econometrics 87(1), 115-143. [ Links ]

Cameron, A. & Trivedi, P. (2009), Microeconometrics using Stata, Stata Press College Station, Texas, United States. [ Links ]

Chudik, A., Pesaran, M. H. & Tosetti, E. (2011), 'Weak and strong cross-section dependence and estimation of large panels', Structural Change and Economic Dynamics 14(1). [ Links ]

Dosi, G. (1988), 'Sources, procedures, and microeconomic effects of innovation', Journal of economic literature XXVI, 1120-1171. [ Links ]

Hoechile, D. (1933), 'Analysis of a complex of statistical variables into principal components', Journal of Educational Psychology (24), 417-520. [ Links ]

Labra, R. & Torrecillas, C. (2014), Guía cero para datos de panel. un enfoque práctico, Working paper 16, Universidad Autónoma de Madrid, Madrid, España. [ Links ]

Lee, K., Pesaran, M. & Pierse, R. (1990), 'Testing for aggregation bias in linear models', Economic Journal (100), 137-150. [ Links ]

Álvarez, I. & Labra, R. (2014), 'Technology Gap and Catching up in Economies Based on Natural Resources: The Case of Chile', Journal of Economics, Business and Management 3(6), 619-627. [ Links ]

Maddala, G. (1971), 'The likelihood approach to pooling cross section and time series data', Econometrica 39(6), 939-953. [ Links ]

Maddala, G. (1975), Some problems arising in pooling cross-section and time-series data, Discussion paper, University of Rochester, Nueva York. [ Links ]

Mairesse, J. & Griliches, Z. (1988), 'Heterogeneity in panel data: are there stable production functions?'. [ Links ]

Mileva, E. (2007), Using Arellano-Bond Dynamic Panel GMM Estimators in Stata, Fordham University, New York. [ Links ]

Nelson, R. & Winter, S. (1982), An evolutionary theory of economic change, Harvard University Press, United States. [ Links ]

Nerlove, M. (1971), 'Further Evidence on the Estimation of Dynamic Economic Relations from a Time Series of Cross Sections', Econometrica, Econometric Society 39(2), 359-382. [ Links ]

Pesaran, M. H. (2006), 'Estimation and inference in large heterogeneous panels with a multifactor error structure', Econometrica 74(4), 967-1012. [ Links ]

Pesaran, M. H., Pierse, R. G. & Kumar, M. S. (1989), 'Econometric analysis of aggregation in the context of linear prediction models', Econometrica: Journal of the Econometric Society (57), 861-888. [ Links ]

Pesaran, M. H. & Smith, R. (1995), 'Estimating long-run relationships from dynamic heterogeneous panels', Journal of econometrics 68(1), 79-113. [ Links ]

Phillips, P. C. B. & Sul, D. (2003), 'Dynamic Panel Estimation and Homogeneity Testing under Cross Section Dependence', The Econometrics Journal (6), 217-259. [ Links ]

Pérez-López, C. (2008), Econometría Avanzada: Técnicas y Herramientas, Pearson Prentice Hall, Madrid, España. [ Links ]

Roodman, D. (2006), How to do xtabond2: An introduction to difference and system GMM in Stata. [ Links ]

Roodman, D. (2009), 'A note on the theme of too many instruments', Oxford Bulletin of Economics and Statistics 71(1), 135-158. [ Links ]

Ruíz-Porras, A. (2012), Econometric research with panel data: History, models and uses in mexico, MPRA-paper 42909, University Library of Munich, Germany. [ Links ]

Santos-Arteaga, F. J., Torrecillas, C. & Tavana, M. (2017), 'Dynamic effects of learning on the innovative outputs and productivity in spanish multinational enterprises', The Journal of Technology Transfer pp. 1-35. [ Links ]

Sargan, J. D. (1958), 'The estimation of economic relationships using instrumental variables', Econometrica: Journal of the Econometric Society 26, 393-415. [ Links ]

Torrecillas, C., Fischer, B. B. & Sánchez, A. (2017), 'The dual role of R&D expenditures in European Union's member states: short-and long-term prospects', Innovation: The European Journal of Social Science Research 30(4), 433-454. [ Links ]

Wooldridge, J. (2013), Introductory Econometrics: A Modern Approach, 5 edn, South-Western, Australia. [ Links ]

Received: 2016; Accepted: 2017

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