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Prediction of Latent Variables in a Mixture of Structural Equation Models, with an Application to the Discrepancy Between Survey and Register Data

  • Erik Meijer
  • Susann Rohwedder
  • Tom Wansbeek

The authors study the prediction of latent variables in a finite mixture of linear structural equation models. The latent variables can be viewed as well-defined variables measured with error or as theoretical constructs that cannot be measured objectively, but for which proxies are observed. The finite mixture component may serve different purposes: it can denote an unobserved segmentation in subpopulations such as market segments, or it can be used as a nonparametric way to estimate an unknown distribution. In the first interpretation, it forms an additional discrete latent variable in an otherwise continuous latent variable model. Different criteria can be employed to derive "optimal" predictors of the latent variables, leading to a taxonomy of possible predictors. The authors derive the theoretical properties of these predictors. Special attention is given to a mixture that includes components with degenerate distributions. They then apply the theory to the optimal estimation of individual earnings when two independent observations are available: one from survey data and one from register data. The discrete components of the model represent observations with or without measurement error, and with either a correct match or a mismatch between the two data sources.

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Paper provided by RAND Corporation Publications Department in its series Working Papers with number 584.

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Length: 44 pages
Date of creation: Jun 2008
Date of revision:
Handle: RePEc:ran:wpaper:584
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  1. Robert F. Phillips, 2003. "Estimation of a Stratified Error-Components Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(2), pages 501-521, 05.
  2. Schneeweiss, Hans & Cheng, Chi-Lun, 2006. "Bias of the structural quasi-score estimator of a measurement error model under misspecification of the regressor distribution," Journal of Multivariate Analysis, Elsevier, vol. 97(2), pages 455-473, February.
  3. repec:ner:tilbur:urn:nbn:nl:ui:12-364321 is not listed on IDEAS
  4. Xin-Yuan Song & Sik-Yum Lee, 2004. "Local Influence Analysis for Mixture of Structural Equation Models," Journal of Classification, Springer, vol. 21(1), pages 111-137, March.
  5. Sik-Yum Lee & Xin-Yuan Song, 2003. "Maximum Likelihood Estimation and Model Comparison for Mixtures of Structural Equation Models with Ignorable Missing Data," Journal of Classification, Springer, vol. 20(2), pages 221-255, September.
  6. Arie Kapteyn & Jelmer Y. Ypma, 2007. "Measurement Error and Misclassification: A Comparison of Survey and Administrative Data," Journal of Labor Economics, University of Chicago Press, vol. 25, pages 513-551.
  7. Jakob de Haan & Erik Leertouwer & Erik Meijer & Tom Wansbeek, 2003. "Measuring central bank independence: a latent variables approach," Scottish Journal of Political Economy, Scottish Economic Society, vol. 50(3), pages 326-340, 08.
  8. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
  9. Gerhard Arminger & Petra Stein & Jörg Wittenberg, 1999. "Mixtures of conditional mean- and covariance-structure models," Psychometrika, Springer, vol. 64(4), pages 475-494, December.
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