Comparisons of Four Methods for Estimating a Dynamic Factor Model

Autor/inn/en	Zhang, Zhiyong; Hamaker, Ellen L.; Nesselroade, John R.
Titel	Comparisons of Four Methods for Estimating a Dynamic Factor Model
Quelle	In: Structural Equation Modeling: A Multidisciplinary Journal, 15 (2008) 3, S.377-402 (26 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1070-5511
Schlagwörter	Structural Equation Models; Simulation; Computer Software; Least Squares Statistics; Computation; Probability; Matrices + Suchen Sie Ihr Suchwort? Simulation program; Simulationsprogramm; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Matrizenrechnung
Abstract	Four methods for estimating a dynamic factor model, the direct autoregressive factor score (DAFS) model, are evaluated and compared. The first method estimates the DAFS model using a Kalman filter algorithm based on its state space model representation. The second one employs the maximum likelihood estimation method based on the construction of a block-Toeplitz covariance matrix in the structural equation modeling framework. The third method is built in the Bayesian framework and implemented using Gibbs sampling. The fourth is the least squares method, which also employs the block-Toeplitz matrix. All 4 methods are implemented in currently available software. The simulation study shows that all 4 methods reach appropriate parameter estimates with comparable precision. Differences among the 4 estimation methods and related software are discussed. (Contains 1 figure, 5 tables and 7 footnotes.) (As Provided).
Anmerkungen	Psychology Press. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals/default.html
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10