Small versus big-data factor extraction in Dynamic Factor Models: An empirical assessment

Pilar Poncela
Authors registered in the RePEc Author Service: Esther Ruiz ()

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

Abstract: In the context of Dynamic Factor Models (DFM), we compare point and interval estimates of the underlying unobserved factors extracted using small and big-data procedures. Our paper differs from previous works in the related literature in several ways. First, we focus on factor extraction rather than on prediction of a given variable in the system. Second, the comparisons are carried out by implementing the procedures considered to the same data. Third, we are interested not only on point estimates but also on confidence intervals for the factors. Based on a simulated system and the macroeconomic data set popularized by Stock and Watson (2012), we show that, for a given procedure, factor estimates based on different cross-sectional dimensions are highly correlated. On the other hand, given the cross-sectional dimension, the Maximum Likelihood Kalman filter and smoother (KFS) factor estimates are highly correlated with those obtained using hybrid Principal Components (PC) and KFS procedures. The PC estimates are somehow less correlated. Finally, the PC intervals based on asymptotic approximations are unrealistically tiny.

Keywords: Confidence; intervals; Principal; components; Quasi-Maximum; Likelihood; Kalman; filter; Sectorial; Factors (search for similar items in EconPapers)
Date: 2015-01-01
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Chapter: Small- Versus Big-Data Factor Extraction in Dynamic Factor Models: An Empirical Assessment (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:ws1502

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