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Analytic Standard Errors for Exploratory Process Factor Analysis

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  • Guangjian Zhang
  • Michael Browne
  • Anthony Ong
  • Sy Chow

Abstract

Exploratory process factor analysis (EPFA) is a data-driven latent variable model for multivariate time series. This article presents analytic standard errors for EPFA. Unlike standard errors for exploratory factor analysis with independent data, the analytic standard errors for EPFA take into account the time dependency in time series data. In addition, factor rotation is treated as the imposition of equality constraints on model parameters. Properties of the analytic standard errors are demonstrated using empirical and simulated data. Copyright The Psychometric Society 2014

Suggested Citation

  • Guangjian Zhang & Michael Browne & Anthony Ong & Sy Chow, 2014. "Analytic Standard Errors for Exploratory Process Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 79(3), pages 444-469, July.
    • Handle: RePEc:spr:psycho:v:79:y:2014:i:3:p:444-469
    DOI: 10.1007/s11336-013-9365-x
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    References listed on IDEAS

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    1. Guangjian Zhang & Sy-Miin Chow & Anthony Ong, 2011. "A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series," Psychometrika, Springer;The Psychometric Society, vol. 76(1), pages 77-96, January.
    2. Charles Crawford & George Ferguson, 1970. "A general rotation criterion and its use in orthogonal rotation," Psychometrika, Springer;The Psychometric Society, vol. 35(3), pages 321-332, September.
    3. White, Halbert & Domowitz, Ian, 1984. "Nonlinear Regression with Dependent Observations," Econometrica, Econometric Society, vol. 52(1), pages 143-161, January.
    4. White, Halbert, 1980. "Using Least Squares to Approximate Unknown Regression Functions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 21(1), pages 149-170, February.
    5. Peter Molenaar, 1985. "A dynamic factor model for the analysis of multivariate time series," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 181-202, June.
    6. Robert Jennrich, 1973. "Standard errors for obliquely rotated factor loadings," Psychometrika, Springer;The Psychometric Society, vol. 38(4), pages 593-604, December.
    7. Peter Molenaar & John Nesselroade, 2001. "Rotation in the dynamic factor modeling of multivariate stationary time series," Psychometrika, Springer;The Psychometric Society, vol. 66(1), pages 99-107, March.
    8. Claude Archer & Robert Jennrich, 1973. "Standard errors for rotated factor loadings," Psychometrika, Springer;The Psychometric Society, vol. 38(4), pages 581-592, December.
    9. Watson, Mark W. & Engle, Robert F., 1983. "Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models," Journal of Econometrics, Elsevier, vol. 23(3), pages 385-400, December.
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