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On a new procedure for identifying a dynamic common factor model
Sobre un nuevo procedimiento para identificar un modelo de factores comunes dinámicos
DOI:
https://doi.org/10.15446/rce.v44n1.84816Keywords:
Canonical correlations, Dynamic common factors, Multivariate time series. (en)Correlación canónica, Factores comunes dinámicos, Series de tiempo multivariadas. (es)
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In the context of the exact dynamic common factor model, canonical correlations in a multivariate time series are used to identify the number of latent common factors. In this paper, we establish a relationship between canonical correlations and the autocovariance function of the factor process, in order to modify a pre-established statistical test to detect the number of common factors. In particular, the test power is increased. Additionally, we propose a procedure to identify a vector ARMA model for the factor process, which is based on the so-called simple and partial canonical autocorrelation functions. We illustrate the proposed methodology by means of some simulated examples and a real data application.
En el contexto del modelo exacto de factores comunes dinámicos, las correlaciones canónicas en series de tiempo multivariadas son usadas para identificar el número de factores latentes. En este artículo, establecemos la relación entre correlación canónica y la función de autocovarianza del proceso de los factores, con el fin de modificar una prueba estadística diseñada para identificar el número de factores comunes. En particular, se incrementa la potencia de la prueba. Adicionalmente, proponemos un procedimiento
para identificar el modelo VARMA para el proceso de los factores, el cual está basado en lo que denominamos las funciones de autocorrelación simple y parcial. Ilustramos la metodología propuesta por medio de ejemplos simulados y una aplicación con datos reales.
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1. Angela Caro, Daniel Peña. (2024). Selecting the number of factors in multi‐variate time series. Journal of Time Series Analysis, https://doi.org/10.1111/jtsa.12760.
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