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Bayesian exploratory factor analysis

Author

Listed:
  • Conti, Gabriella
  • Frühwirth-Schnatter, Sylvia
  • Heckman, James J.
  • Piatek, Rémi

Abstract

This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements.

Suggested Citation

  • Conti, Gabriella & Frühwirth-Schnatter, Sylvia & Heckman, James J. & Piatek, Rémi, 2014. "Bayesian exploratory factor analysis," Journal of Econometrics, Elsevier, vol. 183(1), pages 31-57.
  • Handle: RePEc:eee:econom:v:183:y:2014:i:1:p:31-57
    DOI: 10.1016/j.jeconom.2014.06.008
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    Citations

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    Cited by:

    1. repec:pia:review:v:9:y:2018:i:1:n:1 is not listed on IDEAS
    2. Simon Beyeler & Sylvia Kaufmann, 2016. "Factor augmented VAR revisited - A sparse dynamic factor model approach," Working Papers 16.08, Swiss National Bank, Study Center Gerzensee.
    3. Mike Farjam, 2015. "On whom would I want to depend; Humans or nature?," Jena Economic Research Papers 2015-019, Friedrich-Schiller-University Jena.
    4. Klaus Wälde, 2016. "Emotion Research in Economics," Working Papers 1611, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    5. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2016. "Bayesian analysis of static and dynamic factor models: An ex-post approach towards the rotation problem," Journal of Econometrics, Elsevier, vol. 192(1), pages 190-206.
    6. Benjamin Williams, 2018. "Identification of the Linear Factor Model," Working Papers 2018-002, The George Washington University, Department of Economics, Research Program on Forecasting.
    7. Klaus Wälde, "undated". "Stress and Coping - An Economic Approach," Working Papers 1514, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    8. Mareckova, Jana & Pohlmeier, Winfried, 2017. "Noncognitive Skills and Labor Market Outcomes: A Machine Learning Approach," Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168195, Verein für Socialpolitik / German Economic Association.
    9. repec:eee:csdana:v:124:y:2018:i:c:p:220-234 is not listed on IDEAS
    10. Gabriella Conti & James J. Heckman, 2012. "The Economics of Child Well-Being," NBER Working Papers 18466, National Bureau of Economic Research, Inc.
    11. Leung, Dennis & Drton, Mathias, 2016. "Order-invariant prior specification in Bayesian factor analysis," Statistics & Probability Letters, Elsevier, vol. 111(C), pages 60-66.
    12. repec:mup:actaun:actaun_2018066020497 is not listed on IDEAS
    13. Rémi Piatek & Pia Pinger, 2016. "Maintaining (Locus of) Control? Data Combination for the Identification and Inference of Factor Structure Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(4), pages 734-755, June.
    14. Orazio Attanasio & Richard Blundell & Gabriella Conti & Giacomo Mason, 2018. "Inequality in socioemotional skills: a cross-cohort comparison," Working Papers 2018-071, Human Capital and Economic Opportunity Working Group.

    More about this item

    Keywords

    Bayesian factor models; Exploratory factor analysis; Identifiability; Marginal data augmentation; Model expansion; Model selection;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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