Identification of Common Factors in Multivariate Time Series Modeling

• MARIANO GONZÁLEZ ^[1] ; JUAN M. NAVE ^[2]
  1. [1] Universidad CEU Cardenal Herrera

     Universidad CEU Cardenal Herrera

     Valencia, España

     
  2. [2] Universidad de Castilla-La Mancha

     Universidad de Castilla-La Mancha

     Ciudad Real, España

     
• Localización: Revista Colombiana de Estadística, ISSN-e 2389-8976, ISSN 0120-1751, Vol. 38, Nº. 1, 2015, págs. 219-238
• Idioma: inglés
• DOI: 10.15446/rce.v38n1.48812
• Títulos paralelos:
  • Identificación de factores comunes en la modelización multivariante de series temporales
• Enlaces
  • Texto completo
• Resumen
  • español

    Para la modelización multivariante de series temporales no estacionarias es imprescindible conocer el número de factores comunes que definen el comportamiento de las series. La forma tradicional de abordar este problema es el estudio de las relaciones de cointegración entre los datos a travé de las pruebas de la traza y el máximo valor propio, obteniendo el número de relaciones de largo plazo estacionarias. Como alternativa, se pueden emplear modelos factoriales dinámicos que estiman el número de factores comunes, estacionarios o no, que describen el comportamiento de los datos. En este contexto, analizamos empíricamente el resultado de aplicar tales métodos a series simuladas mediante modelos factoriales conocidos, y a datos reales de los mercados financieros. Los resultados muestran que cuando hay factores comunes estacionarios, cuando el número de observaciones se reduce y/o cuando las variables participan en más de una relación de cointegración, la prueba de factores comunes es más potente que las pruebas habituales de cointegración. Estos resultados, junto con la mayor flexibilidad para identificar la matriz de cargas del proceso generador de datos, hacen que los modelos de factores dinímicos sean más adecuados para su utilización en el análisis multivariante.

  • English

    For multivariate time series modelling, it is essential to know the number of common factors that define the behaviour. The traditional approach to this problem is investigating the number of cointegration relations among the data by determining the trace and the maximum eigenvalue and obtaining the number of stationary long-run relations. Alternatively, this problem can be analyzed using dynamic factor models, which involves estimating the number of common factors, both stationary and not, that describe the behaviour of the data. In this context, we empirically analyze the power of such alternative approaches by applying them to time series that are simulated using known factorial models and to financial market data. The results show that when there are stationary common factors, when the number of observations is reduced and/or when the variables are part of more than one cointegration relation, the common factors test is more powerful than the usually applied cointegration tests. These results, together with the greater flexibility to identify the loading matrix of the data generating process, render dynamic factor models more suitable for use in multivariate time series analysis.

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