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Psychology in econometric models: conceptual and methodological foundations

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  • Thum, Anna-Elisabeth

Abstract

Personality, ability, trust, motivation and beliefs determine outcomes in life and in particular those of economic nature such as finding a job or earnings. A problem with this type of determinants is that they are not immanently objectively quantifiable and that there is no intrinsic scale - such as in the case of age, years of education or wages. Often we think of these concepts as complex and several items are needed to capture them. In the measurement sense, we dispose of a more or less noisy set of measures, which indirectly express and measure a concept of interest. This way of conceptualizing is used in latent variables modelling. I examine in this article in how far economic and econometric literature can contribute to specifying a framework of how to use latent variables in economic models. As a semiparametric identification strategy for models with endogeneous latent factors I propose to use existing work on identification in the presence of endogeneous variables and examine which additional assumptions are necessary to apply this strategy for models with latent variables. I discuss several estimation strategies and implement a Bayesian Markov Chain Monte Carlo (MCMC) algorithm.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 52293.

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Date of creation: 20 Dec 2013
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Handle: RePEc:pra:mprapa:52293

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Keywords: latent variable modelling; identification with endogenous regressors; monte carlo markov chain;

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  1. Borghans Lex & Lee Duckworth Angela & Heckman James J. & Weel Bas ter, 2008. "The Economics and Psychology of Personality Traits," ROA Research Memorandum, Maastricht University, Research Centre for Education and the Labour Market (ROA) 001, Maastricht University, Research Centre for Education and the Labour Market (ROA).
  2. Poirier, Dale J., 1998. "Revising Beliefs In Nonidentified Models," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 14(04), pages 483-509, August.
  3. Jeff Douglas, 1997. "Joint consistency of nonparametric item characteristic curve and ability estimation," Psychometrika, Springer, Springer, vol. 62(1), pages 7-28, March.
  4. 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, University of Chicago Press, vol. 24(3), pages 411-482, July.
  5. Carneiro, Pedro & Hansen, Karsten & Heckman, James, 2003. "Estimating distributions of treatment effects with an application to the returns to schooling and measurement of the effects of uncertainty on college choice," Working Paper Series, IFAU - Institute for Evaluation of Labour Market and Education Policy 2003:9, IFAU - Institute for Evaluation of Labour Market and Education Policy.
  6. Lewbel, Arthur, 2000. "Semiparametric qualitative response model estimation with unknown heteroscedasticity or instrumental variables," Journal of Econometrics, Elsevier, Elsevier, vol. 97(1), pages 145-177, July.
  7. Melissa Osborne & Herbert Gintis & Samuel Bowles, 2001. "The Determinants of Earnings: A Behavioral Approach," Journal of Economic Literature, American Economic Association, American Economic Association, vol. 39(4), pages 1137-1176, December.
  8. Yoshio Takane & Jan Leeuw, 1987. "On the relationship between item response theory and factor analysis of discretized variables," Psychometrika, Springer, Springer, vol. 52(3), pages 393-408, September.
  9. 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.
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