Ignoring cross-correlated idiosyncratic components when extracting factors in dynamic factor models

Maria Pilar Poncela Blanco and Diego Eduardo Fresoli

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: In economics, Principal Components, its generalized version that takes into account heteroscedasticity, and Kalman filter and smoothing procedures are among the most popular procedures for factor extraction in the context of Dynamic Factor Models. This paper analyses the consequences on point and interval factor estimation of using these procedures when the idiosyncratic components are wrongly assumed to be cross-sectionally uncorrelated. We show that not taking into account the presence of cross-sectional dependence increases the uncertainty of point estimates of the factors. Furthermore, the Mean Square Errors computed using the usual expressions based on asymptotic approximations, are underestimated and may lead to prediction intervals with extremely low coverages.

Keywords: EM; Algorithm; Kalman; Filter; Principal; Components; State-Space; Model (search for similar items in EconPapers)
JEL-codes: C32 C38 C55 (search for similar items in EconPapers)
Date: 2022-12-12
New Economics Papers: this item is included in nep-ecm and nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:36251

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