Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/167627 
Erscheinungsjahr: 
2011
Verlag: 
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften, Leibniz-Informationszentrum Wirtschaft, Kiel und Hamburg
Zusammenfassung: 
This paper investigates the finite sample properties of the two-step estimators of dynamic factor models when unobservable common factors are estimated by the principal components methods in the first step. Effects of the number of individual series on the estimation of an auto-regressive model of a common factor are investigated both by theoretical analysis and by a Monte Carlo simulation. When the number of the series is not sufficiently large relative to the number of time series observations, the auto-regressive coefficient estimator of positively auto-correlated factor is biased downward and the bias is larger for a more persistent factor. In such a case, bootstrap procedures are effective in reducing the bias and bootstrap confidence intervals outperform naive asymptotic confidence intervals in terms of controlling the coverage probability.
Schlagwörter: 
Bias Correction
Bootstrap
Dynamic Factor Model
Principal Components
JEL: 
C15
C53
Dokumentart: 
Preprint

Datei(en):
Datei
Größe
782.83 kB





Publikationen in EconStor sind urheberrechtlich geschützt.