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Semiparametric Methods for the Measurement of Latent Attitudes and the Estimation of Their Behavioural Consequences

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Listed:
  • Richard H. Spady

Abstract

We model attitudes as latent variables that induce stochastic dominance relations in (item) responses. Observable characteristics that affect attitudes can be incorporated into the analysis to improve the measurement of the attitudes; the measurements are posterior distributions that condition on the responses and characteristics of each respondent. Methods to use these measurements to characterize the relation between attitudes and behaviour are developed and implemented.

Suggested Citation

  • Richard H. Spady, 2007. "Semiparametric Methods for the Measurement of Latent Attitudes and the Estimation of Their Behavioural Consequences," Economics Working Papers ECO2007/29, European University Institute.
    • Handle: RePEc:eui:euiwps:eco2007/29
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    References listed on IDEAS

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    1. 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.
    2. 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 of Labor Economics (IZA).
    3. 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.
    4. Karim Chalak & Halbert White, 2007. "An Extended Class of Instrumental Variables for the Estimation of Causal Effects," Boston College Working Papers in Economics 692, Boston College Department of Economics, revised 30 Nov 2009.
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    Cited by:

    1. Mayssun El-Attar, 2013. "Trust, child care technology choice and female labor force participation," Review of Economics of the Household, Springer, vol. 11(4), pages 507-544, December.
    2. Benjamin Williams, 2019. "Identification of a nonseparable model under endogeneity using binary proxies for unobserved heterogeneity," Quantitative Economics, Econometric Society, vol. 10(2), pages 527-563, May.
    3. Hendrik Thiel & Stephan L. Thomsen & Bettina Büttner, 2014. "Variation of learning intensity in late adolescence and the effect on personality traits," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(4), pages 861-892, October.
    4. Thiel, Hendrik & Thomsen, Stephan L., 2013. "Noncognitive skills in economics: Models, measurement, and empirical evidence," Research in Economics, Elsevier, vol. 67(2), pages 189-214.
    5. Thum, Anna-Elisabeth, 2013. "Psychology in econometric models: conceptual and methodological foundations," MPRA Paper 52293, University Library of Munich, Germany.
    6. El-Attar, Mayssun, 2009. "Could Education Promote the Israeli-Palestinian Peace Process?," IZA Discussion Papers 4447, Institute of Labor Economics (IZA).
    7. Christoph T. Weiss, 2012. "Persistent Attitudes and Behaviors," "Marco Fanno" Working Papers 0143, Dipartimento di Scienze Economiche "Marco Fanno".
    8. Mayssun El-Attar & Markus Poschke, 2011. "Trust and the Choice Between Housing and Financial Assets: Evidence from Spanish Households," Review of Finance, European Finance Association, vol. 15(4), pages 727-756.
    9. Schennach, Susanne M., 2020. "Mismeasured and unobserved variables," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 487-565, Elsevier.

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    More about this item

    Keywords

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    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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