IDEAS home Printed from https://ideas.repec.org/p/jku/nrnwps/2014_08.html
   My bibliography  Save this paper

Bayesian Exploratory Factor Analysis

Author

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

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

  • Gabriella Conti & Sylvia Frühwirth-Schnatter & James J. Heckman & Rémi Piatek, 2014. "Bayesian Exploratory Factor Analysis," NRN working papers 2014-08, The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria.
  • Handle: RePEc:jku:nrnwps:2014_08
    as

    Download full text from publisher

    File URL: http://www.labornrn.at/wp/2014/wp1408.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-1339, November.
    2. George Ferguson, 1954. "The concept of parsimony in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 19(4), pages 281-290, December.
    3. Juan D. Barón & Deborah Cobb-Clark, 2010. "Are Young People's Educational Outcomes Linked to their Sense of Control?," Borradores de Economia 599, Banco de la Republica de Colombia.
    4. Mario Fiorini & Michael P. Keane, 2014. "How the Allocation of Children's Time Affects Cognitive and Noncognitive Development," Journal of Labor Economics, University of Chicago Press, vol. 32(4), pages 787-836.
    5. Pedro Carneiro & Karsten T. Hansen & James J. Heckman, 2003. "Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College," NBER Working Papers 9546, National Bureau of Economic Research, Inc.
    6. Stéphane Bonhomme & Jean-Marc Robin, 2010. "Generalized Non-Parametric Deconvolution with an Application to Earnings Dynamics," Review of Economic Studies, Oxford University Press, vol. 77(2), pages 491-533.
    7. Robert Jennrich, 2002. "A simple general method for oblique rotation," Psychometrika, Springer;The Psychometric Society, vol. 67(1), pages 7-19, March.
    8. Geweke, John & Zhou, Guofu, 1996. "Measuring the Pricing Error of the Arbitrage Pricing Theory," Review of Financial Studies, Society for Financial Studies, vol. 9(2), pages 557-587.
    9. Carneiro, Pedro & Hansen, Karsten T. & Heckman, James J., 2003. "Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College Choice," IZA Discussion Papers 767, Institute for the Study of Labor (IZA).
    10. Aguilar, Omar & West, Mike, 2000. "Bayesian Dynamic Factor Models and Portfolio Allocation," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(3), pages 338-357, July.
    11. Flavio Cunha & James J. Heckman, 2008. "Formulating, Identifying and Estimating the Technology of Cognitive and Noncognitive Skill Formation," Journal of Human Resources, University of Wisconsin Press, vol. 43(4).
    12. Pedro Carneiro & Karsten T. Hansen & James J. Heckman, 2003. "2001 Lawrence R. Klein Lecture Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College Choice," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(2), pages 361-422, May.
    13. Jo Blanden & Paul Gregg & Lindsey Macmillan, 2007. "Accounting for Intergenerational Income Persistence: Noncognitive Skills, Ability and Education," Economic Journal, Royal Economic Society, vol. 117(519), pages 43-60, March.
    14. Gabriella Conti & James Heckman & Sergio Urzua, 2010. "The Education-Health Gradient," American Economic Review, American Economic Association, vol. 100(2), pages 234-238, May.
    15. Alexei Onatski, 2009. "Testing Hypotheses About the Number of Factors in Large Factor Models," Econometrica, Econometric Society, vol. 77(5), pages 1447-1479, September.
    16. Robert Kaestner & Kevin Callison, 2011. "Adolescent Cognitive and Noncognitive Correlates of Adult Health," Journal of Human Capital, University of Chicago Press, vol. 5(1), pages 29-69.
    17. Flavio Cunha & James J. Heckman & Susanne M. Schennach, 2010. "Estimating the Technology of Cognitive and Noncognitive Skill Formation," Econometrica, Econometric Society, vol. 78(3), pages 883-931, May.
    18. James J. Heckman & Jora Stixrud & Sergio Urzua, 2006. "The Effects of Cognitive and Noncognitive Abilities on Labor Market Outcomes and Social Behavior," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 411-482, July.
    19. Imai, Kosuke & van Dyk, David A., 2005. "A Bayesian analysis of the multinomial probit model using marginal data augmentation," Journal of Econometrics, Elsevier, vol. 124(2), pages 311-334, February.
    20. Mario Fiorini & Michael P. Keane, 2014. "How the Allocation of Children's Time Affects Cognitive and Noncognitive Development," Journal of Labor Economics, University of Chicago Press, vol. 32(4), pages 787 - 836.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. repec:eee:csdana:v:124:y:2018:i:c:p:220-234 is not listed on IDEAS
    2. Conti, Gabriella & Heckman, James J., 2012. "The Economics of Child Well-Being," IZA Discussion Papers 6930, Institute for the Study of Labor (IZA).
    3. Klaus Wälde, "undated". "Stress and Coping - An Economic Approach," Working Papers 1514, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    4. 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.
    5. Mike Farjam, 2015. "On whom would I want to depend; Humans or nature?," Jena Economic Research Papers 2015-019, Friedrich-Schiller-University Jena.
    6. Klaus Wälde, 2016. "Emotion Research in Economics," Working Papers 1611, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    7. Leung, Dennis & Drton, Mathias, 2016. "Order-invariant prior specification in Bayesian factor analysis," Statistics & Probability Letters, Elsevier, vol. 111(C), pages 60-66.
    8. 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.
    9. 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.
    10. 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.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jku:nrnwps:2014_08. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (René Böheim). General contact details of provider: http://edirc.repec.org/data/aclawat.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.