IDEAS home Printed from https://ideas.repec.org/p/msh/ebswps/2012-12.html
   My bibliography  Save this paper

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

Author

Listed:
  • Timothy A. Weterings
  • Mark N. Harris
  • Bruce Hollingsworth

Abstract

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.

Suggested Citation

  • Timothy A. Weterings & Mark N. Harris & Bruce Hollingsworth, 2012. "Extending Unobserved Heterogeneity - A Strategy for Accounting for Respondent Perceptions in the Absence of Suitable Data," Monash Econometrics and Business Statistics Working Papers 12/12, Monash University, Department of Econometrics and Business Statistics.
  • Handle: RePEc:msh:ebswps:2012-12
    as

    Download full text from publisher

    File URL: http://business.monash.edu/econometrics-and-business-statistics/research/publications/ebs/wp12-12.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stephen V. Cameron & James J. Heckman, 1998. "Life Cycle Schooling and Dynamic Selection Bias: Models and Evidence for Five Cohorts of American Males," Journal of Political Economy, University of Chicago Press, vol. 106(2), pages 262-333, April.
    2. 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.
    3. 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.
    4. 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.
    5. Stefan Boes & Rainer Winkelmann, 2006. "Ordered Response Models," Springer Books, in: Olaf Hübler & Jachim Frohn (ed.), Modern Econometric Analysis, chapter 12, pages 167-181, Springer.
    6. repec:cup:apsrev:v:98:y:2004:i:01:p:191-207_00 is not listed on IDEAS
    7. 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.
    8. 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.
    9. 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).
    10. 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, February.
    11. Greene,William H. & Hensher,David A., 2010. "Modeling Ordered Choices," Cambridge Books, Cambridge University Press, number 9780521142373.
    12. Stephen V. Cameron & James J. Heckman, 1998. "Life Cycle Schooling and Dynamic Selection Bias: Models and Evidence for Five Cohorts," NBER Working Papers 6385, National Bureau of Economic Research, Inc.
    13. Greene,William H. & Hensher,David A., 2010. "Modeling Ordered Choices," Cambridge Books, Cambridge University Press, number 9780521194204.
    14. Ziebarth, Nicolas, 2010. "Measurement of health, health inequality, and reporting heterogeneity," Social Science & Medicine, Elsevier, vol. 71(1), pages 116-124, July.
    15. 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.
    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. Sandeep Mohapatra & Leo Simon, 2017. "Intra-household bargaining over household technology adoption," Review of Economics of the Household, Springer, vol. 15(4), pages 1263-1290, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. William Greene, 2014. "Models for ordered choices," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 15, pages 333-362, Edward Elgar Publishing.
    2. Corrado, L. & Weeks, M., 2010. "Identification Strategies in Survey Response Using Vignettes," Cambridge Working Papers in Economics 1031, Faculty of Economics, University of Cambridge.
    3. Udo Schneider & Christian Pfarr & Brit Schneider & Volker Ulrich, 2012. "I feel good! Gender differences and reporting heterogeneity in self-assessed health," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 13(3), pages 251-265, June.
    4. William H. Greene & David A. Hensher, 2008. "Modeling Ordered Choices: A Primer and Recent Developments," Working Papers 08-26, New York University, Leonard N. Stern School of Business, Department of Economics.
    5. Lars Thiel, 2014. "Illness and Health Satisfaction: The Role of Relative Comparisons," SOEPpapers on Multidisciplinary Panel Data Research 695, DIW Berlin, The German Socio-Economic Panel (SOEP).
    6. Stefan Boes, 2013. "Nonparametric analysis of treatment effects in ordered response models," Empirical Economics, Springer, vol. 44(1), pages 81-109, February.
    7. William H. Greene & Mark N. Harris & Rachel J. Knott & Nigel Rice, 2021. "Specification and testing of hierarchical ordered response models with anchoring vignettes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 31-64, January.
    8. Franco Peracchi & Claudio Rossetti, 2013. "The heterogeneous thresholds ordered response model: identification and inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(3), pages 703-722, June.
    9. Xu, Ke-Li, 2018. "A semi-nonparametric estimator of regression discontinuity design with discrete duration outcomes," Journal of Econometrics, Elsevier, vol. 206(1), pages 258-278.
    10. Christian Pfarr & Andreas Schmid & Udo Schneider, 2011. "Estimating ordered categorical variables using panel data: a generalized ordered probit model with an autofit procedure," Journal of Economics and Econometrics, Economics and Econometrics Society, vol. 54(1), pages 7-23.
    11. Tatiana Komarova & William Matcham, 2022. "Multivariate ordered discrete response models," Papers 2205.05779, arXiv.org, revised Mar 2023.
    12. William Greene & Mark N. Harris & Bruce Hollingsworth & Rachel Knott & Nigel Rice, 2016. "Reporting heterogeneity effects in modelling self reports of health," Working Papers 16-12, New York University, Leonard N. Stern School of Business, Department of Economics.
    13. Greene, William & Harris, Mark N. & Knott, Rachel & Rice, Nigel, 2023. "Reporting heterogeneity in modeling self-assessed survey outcomes," Economic Modelling, Elsevier, vol. 124(C).
    14. Pfarr, Christian & Schmid, Andreas & Schneider, Udo, 2011. "Reporting Heterogeneity in Self-Assessed Health among Elderly Europeans: The Impact of Mental and Physical Health Status," MPRA Paper 29900, University Library of Munich, Germany.
    15. Heckman, James J. & Urzúa, Sergio, 2010. "Comparing IV with structural models: What simple IV can and cannot identify," Journal of Econometrics, Elsevier, vol. 156(1), pages 27-37, May.
    16. Han, Sukjin, 2021. "Identification in nonparametric models for dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 225(2), pages 132-147.
    17. Chen, Le-Yu & Oparina, Ekaterina & Powdthavee, Nattavudh & Srisuma, Sorawoot, 2022. "Robust Ranking of Happiness Outcomes: A Median Regression Perspective," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 672-686.
    18. Davillas, Apostolos & de Oliveira, Victor Hugo & Jones, Andrew M., 2023. "Is inconsistent reporting of self-assessed health persistent and systematic? Evidence from the UKHLS," Economics & Human Biology, Elsevier, vol. 49(C).
    19. Davillas, Apostolos & Burlinson, Andrew & Liu, Hui-Hsuan, 2022. "Getting warmer: Fuel poverty, objective and subjective health and well-being," Energy Economics, Elsevier, vol. 106(C).
    20. Sayed Alim Samim & Zhiquan Hu & Sebastian Stepien & Sayed Younus Amini & Ramin Rayee & Kunyu Niu & George Mgendi, 2021. "Food Insecurity and Related Factors among Farming Families in Takhar Region, Afghanistan," Sustainability, MDPI, vol. 13(18), pages 1-17, September.

    More about this item

    Keywords

    Ordered Choice Modeling; Unobserved Heterogeneity; Simulated Maximum Likelihood;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    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:msh:ebswps:2012-12. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Professor Xibin Zhang (email available below). General contact details of provider: https://edirc.repec.org/data/dxmonau.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.