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Extending Unobserved Heterogeneity - A Strategy for Accounting for Respondent Perceptions in the Absence of Suitable Data

Listed author(s):
  • Timothy A. Weterings

    ()

  • Mark N. Harris
  • Bruce Hollingsworth

This research proposes that, in cases where threshold covariates are either unavailable or difficult to observe, practitioners should treat these characteristics as latent, and use simulated maximum likelihood techniques to control for them. Two econometric frameworks for doing so in a more flexible manner are proposed. The finite sample performance of these new specifications are investigated with the use of Monte Carlo simulation. Applications of successively more flexible models are then given, with extensive post-estimation analysis utilised to better assess the likely implications of model choice on conclusions made in empirical research.

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File URL: http://business.monash.edu/econometrics-and-business-statistics/research/publications/ebs/wp12-12.pdf
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Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 12/12.

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Length: 31 pages
Date of creation: May 2012
Handle: RePEc:msh:ebswps:2012-12
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Web page: http://business.monash.edu/econometrics-and-business-statistics
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  1. García-Gómez, Pilar & Jones, Andrew M. & Rice, Nigel, 2010. "Health effects on labour market exits and entries," Labour Economics, Elsevier, vol. 17(1), pages 62-76, January.
  2. Flavio Cunha & James J. Heckman & Salvador Navarro, 2007. "The Identification And Economic Content Of Ordered Choice Models With Stochastic Thresholds," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(4), pages 1273-1309, November.
  3. Stephen Pudney & Michael Shields, 2000. "Gender, race, pay and promotion in the British nursing profession: estimation of a generalized ordered probit model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(4), pages 367-399.
  4. Stefan Boes & Rainer Winkelmann, 2006. "Ordered response models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 167-181, March.
  5. Pfeifer, Christian & Cornelißen, Thomas, 2010. "The impact of participation in sports on educational attainment--New evidence from Germany," Economics of Education Review, Elsevier, vol. 29(1), pages 94-103, February.
  6. Litchfield, Julie & Reilly, Barry & Veneziani, Mario, 2012. "An analysis of life satisfaction in Albania: An heteroscedastic ordered probit model approach," Journal of Economic Behavior & Organization, Elsevier, vol. 81(3), pages 731-741.
  7. Nicolas R. Ziebarth, 2009. "Measurement of Health, the Sensitivity of the Concentration Index, and Reporting Heterogeneity," SOEPpapers on Multidisciplinary Panel Data Research 211, DIW Berlin, The German Socio-Economic Panel (SOEP).
  8. William Greene & Mark N. Harris & Bruce Hollingsworth & Timothy A. Weterings, 2014. "Heterogeneity In Ordered Choice Models: A Review With Applications To Self-Assessed Health," Journal of Economic Surveys, Wiley Blackwell, vol. 28(1), pages 109-133, 02.
  9. Greene,William H. & Hensher,David A., 2010. "Modeling Ordered Choices," Cambridge Books, Cambridge University Press, number 9780521194204, December.
  10. Ziebarth, Nicolas, 2010. "Measurement of health, health inequality, and reporting heterogeneity," Social Science & Medicine, Elsevier, vol. 71(1), pages 116-124, July.
  11. Powdthavee, Nattavudh, 2009. "Ill-health as a household norm: Evidence from other people's health problems," Social Science & Medicine, Elsevier, vol. 68(2), pages 251-259, January.
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