Dynamic factor models: Does the specification matter?
EntityUAM. Departamento de Análisis Económico: Economía Cuantitativa
10.1007/s13209-021-00248-2Series-Journal Of The Spanish Economic Association (2021): 1-32
ISSN1869-4187 (print); 1869-4195 (online)
Funded byThis study was funded by the Spanish National Research Agency, Ministry of Science and Technology (Grant Nos. PID2019-108079GB-C21/AIE/10.13039/501100011033 and PID2019-108079GBC22/AIE/10.13039/501100011033)
ProjectGobierno de España. PID2019-108079GB-C21/AIE/10.13039/501100011033; Gobierno de España. PID2019-108079GBC22/AIE/10.13039/501100011033
SubjectsEM algorithm; Kalman filter; Macroeconomic forecasting; Principal components; State-space model; Economía
Rights© The Author(s)
Esta obra está bajo una Licencia Creative Commons Atribución 4.0 Internacional.
Dynamic factor models (DFMs), which assume the existence of a small number of unobserved underlying factors common to a large number of variables, are very popular among empirical macroeconomists. Factors can be extracted using either nonparametric principal components or parametric Kalman filter and smoothing procedures, with the former being computationally simpler and robust against misspecification and the latter coping in a natural way with missing and mixed-frequency data, time-varying parameters, nonlinearities and non-stationarity, among many other stylized facts often observed in real systems of economic variables. This paper analyses the empirical consequences on factor estimation, in-sample predictions and out-of-sample forecasting of using alternative estimators of the DFM under various sources of potential misspecification. In particular, we consider factor extraction when assuming different number of factors and different factor dynamics. The factors are extracted from a popular data base of US macroeconomic variables, widely analyzed in the literature without consensus about the most appropriate model specification. We show that this lack of consensus is only marginally crucial when it comes to factor extraction, but it matters when the objective is out-of-sample forecasting.
Google Scholar:Miranda Gualdrón, Karen Alejandra - Poncela Blanco, Pilar - Ruiz Uceda, María Esther
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