Thum, AnnaElisabeth (2013): Psychology in econometric models: conceptual and methodological foundations.

PDF
MPRA_paper_52293.pdf Download (201kB)  Preview 
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.
Item Type:  MPRA Paper 

Original Title:  Psychology in econometric models: conceptual and methodological foundations 
Language:  English 
Keywords:  latent variable modelling, identification with endogenous regressors, monte carlo markov chain 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C11  Bayesian Analysis: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C14  Semiparametric and Nonparametric Methods: General C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C38  Classification Methods ; Cluster Analysis ; Principal Components ; Factor Models J  Labor and Demographic Economics > J2  Demand and Supply of Labor > J24  Human Capital ; Skills ; Occupational Choice ; Labor Productivity 
Item ID:  52293 
Depositing User:  Dr Anna Elisabeth Thum 
Date Deposited:  21 Dec 2013 09:11 
Last Modified:  28 Mar 2017 02:22 
References:  Albert, J.H. & Chib, S. (1993) : Bayesian Analysis of Binary and Polychotomous Response Data, Journal of the American Statistical Association, 88 (422), 669679 Berry, D. A. (1996) : Statistics: A Bayesian Perspective. Duxbury, London, UK. Blundell, R. & Powell, C. (2003) : Endogeneity in Semiparametric Binary Response Models, The Review of Economic Studies, 71 (3), 655679. Breiman, L. (1992) : Probability. SIAM, Philadelphia, USA. Borghans, L.; Duckworth, A.L.; Heckman, J. & terWeel, B. (2008) : The Economics of Psychology and Personality Traits, Journal of Human Resources. Bowles, S.; Gintis, H. & Osborne, M. (2001) : The determinants of earnings: A Behavioral Approach, Journal of Economic Literature 39 (4), 11371176. Canerio, P.; Hansen, K & Heckman J. (2003) : Estimating Distributions of Treatment Effects with an Application to the returns of Schooling and Measurement of the Effects of Uncertainty of College Choice, International Economic Review 44(2), 361442 Cowles, M. K. & Carlin, B.P. (1996) : Markov Chain Monte Carlo convergence diagnostics: a comparative review. Journal of the American Statistical Association 91, 883904. Chesher, A. (2007) : Endogeneity and discrete outcomes. cemmap Working Papers (CWP05/07). Institute for Fiscal Studies, London, UK. DeLeeuw, J. & Takane,Y. (1987) : On the Relationship between Item Response Theory and Factor Analysis of Discretized Variables, Psychometrika 52 (3). Douglas, J. (1997) : Joint Consistency of Nonparametric Item Characteristic Curve and Ability Estimation, Psychometrika 62 (1). Fahrmeir, L. & Raach, A. (2006) : A Bayesian semiparametric latent variable model for mixed responses, Psychometrika. Gelfand, A. E. & Smith, A. F. M. (1990) : Samplingbased approaches to calculating marginal densities. Journal of the American Statistical Association 85, 398  409. Gilks, W. R.; Richardson, S. & Spiegelhalter, D. J. (Eds.) : Markov Chain Monte Carlo in practice. Chapman and Hall. Heckman J.; Stixrud, J. & Urzua, S. (2006) : The Effects of Cognitive and Noncognitive Abilities on Labor Market Outcomes and Social Behavior, Journal of Labor Economics. Holland, P.W. & Rosenbaum, P.R. (1986) : Conditional Association and Unidimensionality in Monotone Latent Variable Models, Annals of Statistics 14 (4). Imbens, G. (2009) : New Developments in Econometrics, Lecture 13, Bayesian Inference, cemmap lectures, University College London. Lee, P. M. (2004) : Bayesian statistics: an introduction (3rd edition). Arnold, London. Lewbel, A. (2000) : Semiparametric qualitative response model estimation with unknown heteroscedasticity or instrumental variables, Journal of Econometrics 97 (1), 145177. Matzkin, R. (2003) : Unobservable Instruments, mimeo, Northwestern University. Matzkin, R. (2007) : Nonparametric Identification, Handbook of Econometrics Vol 6B. Mokken, R. J. (1971) : A Theory and Procedure of Scale Analysis, Berlin, Germany: De Gruyter. Poirier, D. J. (1998) : Revising Beliefs in Nonidentified Models, Econometric Theory, 14, 483509. Molenaar, I.W. & Sijtsma, K. (2002) : Introduction to nonparametric item response theory, Sage Publications, Thousand Oaks, USA. RabeHesketh, S. & Skondral, A. (2004) : Generalized Latent Variable Modeling: Multilevel, Longitudinal and Structural Equation Models. Boca Raton, FL: Chapman & Hall/CRC. Raach, A. (2005) : A Bayesian semiparametric latent variable model for binary, ordinal and continuous response, Thesis LudwigMaximilians Universitaet Muenchen, Germany. Robert, C.P. & Casella, G. (2004) : Monte Carlo statistical methods (2nd edition). Springer, New York. Rosenbaum,P.R. (1984) : Testing Conditional Independence and Monotonicity Assumptions in Item Response Theory, Psychometrika 49 (3). Rupp, A. ; Dey, D. K. & Zumbo, B. D. (2004) : To Bayes or not to Bayes, from whether to when: applications of Bayesian methodology to modeling, Structural Equation Modeling 11, 424451. Spady, R. (2006) : Identification and estimation of latent attitudes and their behavioral implications, cemmap Working Papers (CWP12/06). Institute for Fiscal Studies, London, UK. Spady, R. (2007) : Semiparametric Methods for the Measurement of Latent Attitudes and the Estimation of their Behavioral Consequences, cemmap Working Papers (CWP26/07). Institute for Fiscal Studies, London, UK. Wansbeek, T. (2000) : Measurement Error and Latent Variables in Econometrics, North Holland. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/52293 