IDEAS home Printed from https://ideas.repec.org/a/bpj/jecome/v11y2022i1p35-53n8.html
   My bibliography  Save this article

Modeling Qualitative Outcomes by Supplementing Participant Data with General Population Data: A New and More Versatile Approach

Author

Listed:
  • Erard Brian

    (B. Erard & Associates, LLC, Reston, VA, USA)

Abstract

Although one often has detailed information about participants in a program, the lack of comparable information on non-participants precludes standard qualitative choice estimation. This challenge can be overcome by incorporating a supplementary sample of covariate values from the general population. This paper presents new estimators based on this sampling strategy, which perform comparably to the best existing supplementary sampling estimators. The key advantage of the new estimators is that they readily incorporate sample weights, so that they can be applied to Census surveys and other supplementary data sources that have been generated using complex sample designs. This substantially widens the range of problems that can be addressed under a supplementary sampling estimation framework. The potential for improving precision by incorporating imperfect knowledge of the population prevalence rate is also explored.

Suggested Citation

  • Erard Brian, 2022. "Modeling Qualitative Outcomes by Supplementing Participant Data with General Population Data: A New and More Versatile Approach," Journal of Econometric Methods, De Gruyter, vol. 11(1), pages 35-53, January.
    • Handle: RePEc:bpj:jecome:v:11:y:2022:i:1:p:35-53:n:8
    DOI: 10.1515/jem-2021-0004
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jem-2021-0004
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/jem-2021-0004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Imbens, Guido W, 1992. "An Efficient Method of Moments Estimator for Discrete Choice Models with Choice-Based Sampling," Econometrica, Econometric Society, vol. 60(5), pages 1187-1214, September.
    2. Gill Ward & Trevor Hastie & Simon Barry & Jane Elith & John R. Leathwick, 2009. "Presence-Only Data and the EM Algorithm," Biometrics, The International Biometric Society, vol. 65(2), pages 554-563, June.
    3. Robert Rosenman & Scott Goates & Laura Hill, 2012. "Participation in universal prevention programmes," Applied Economics, Taylor & Francis Journals, vol. 44(2), pages 219-228, January.
    4. Lancaster, Tony & Imbens, Guido, 1996. "Case-control studies with contaminated controls," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 145-160.
    5. Barry C. Burden & David T. Canon & Kenneth R. Mayer & Donald P. Moynihan, 2014. "Election Laws, Mobilization, and Turnout: The Unanticipated Consequences of Election Reform," American Journal of Political Science, John Wiley & Sons, vol. 58(1), pages 95-109, 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. Erard, Brian & Langetieg, Patrick & Payne, Mark & Plumley, Alan, 2020. "Ghosts in the Income Tax Machinery," MPRA Paper 100036, University Library of Munich, Germany.

    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. Sung Jae Jun & Sokbae Lee, 2020. "Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions," Papers 2004.08318, arXiv.org, revised Oct 2023.
    2. Esmerelda A. Ramalho & Richard Smith, 2003. "Discrete choice non-response," CeMMAP working papers 07/03, Institute for Fiscal Studies.
    3. Sung Jae Jun & Sokbae (Simon) Lee, 2020. "Causal inference in case-control studies," CeMMAP working papers CWP19/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Lee, Kangbok & Joo, Sunghoon & Baik, Hyeoncheol & Han, Sumin & In, Joonhwan, 2020. "Unbalanced data, type II error, and nonlinearity in predicting M&A failure," Journal of Business Research, Elsevier, vol. 109(C), pages 271-287.
    5. Adam M. Kleinbaum & Toby E. Stuart & Michael L. Tushman, 2013. "Discretion Within Constraint: Homophily and Structure in a Formal Organization," Organization Science, INFORMS, vol. 24(5), pages 1316-1336, October.
    6. Ramalho, Esmeralda A., 2007. "Binary models with misclassification in the variable of interest and nonignorable nonresponse," Economics Letters, Elsevier, vol. 96(1), pages 70-76, July.
    7. Esmeralda A. Ramalho & Richard J. Smith, 2013. "Discrete Choice Non-Response," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 80(1), pages 343-364.
    8. Nail Kashaev, 2022. "Estimation of Parametric Binary Outcome Models with Degenerate Pure Choice-Based Data with Application to COVID-19-Positive Tests from British Columbia," University of Western Ontario, Departmental Research Report Series 20225, University of Western Ontario, Department of Economics.
    9. Shi Chang & Rohan Singh Wilkho & Nasir Gharaibeh & Garett Sansom & Michelle Meyer & Francisco Olivera & Lei Zou, 2023. "Environmental, climatic, and situational factors influencing the probability of fatality or injury occurrence in flash flooding: a rare event logistic regression predictive model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 3957-3978, April.
    10. Małgorzata Łazęcka & Jan Mielniczuk & Paweł Teisseyre, 2021. "Estimating the class prior for positive and unlabelled data via logistic regression," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(4), pages 1039-1068, December.
    11. Butler, J. S., 2000. "Efficiency results of MLE and GMM estimation with sampling weights," Journal of Econometrics, Elsevier, vol. 96(1), pages 25-37, May.
    12. Erard, Brian, 2017. "Modeling Qualitative Outcomes by Supplementing Participant Data with General Population Data: A Calibrated Qualitative Response Estimation Approach," MPRA Paper 79927, University Library of Munich, Germany.
    13. Wenkai Li & Yuanchi Liu & Ziyue Liu & Zhen Gao & Huabing Huang & Weijun Huang, 2022. "A Positive-Unlabeled Learning Algorithm for Urban Flood Susceptibility Modeling," Land, MDPI, vol. 11(11), pages 1-17, November.
    14. Lancaster, Tony, 1997. "Bayes WESML Posterior inference from choice-based samples," Journal of Econometrics, Elsevier, vol. 79(2), pages 291-303, August.
    15. Robert M. Dorazio, 2012. "Predicting the Geographic Distribution of a Species from Presence-Only Data Subject to Detection Errors," Biometrics, The International Biometric Society, vol. 68(4), pages 1303-1312, December.
    16. Esmeralda Ramalho, 2004. "Covariate Measurement Error in Endogenous Stratified Samples," Economics Working Papers 2_2004, University of Évora, Department of Economics (Portugal).
    17. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    18. Masahiro Kato & Shota Yasui, 2020. "Learning Classifiers under Delayed Feedback with a Time Window Assumption," Papers 2009.13092, arXiv.org, revised Jun 2022.
    19. Erard, Brian & Langetieg, Patrick & Payne, Mark & Plumley, Alan, 2020. "Ghosts in the Income Tax Machinery," MPRA Paper 100036, University Library of Munich, Germany.
    20. Lancaster, Tony & Imbens, Guido, 1996. "Case-control studies with contaminated controls," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 145-160.

    More about this item

    Keywords

    qualitative response; discrete choice; choice-based sampling; supplementary sampling; contaminated controls;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: 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

    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:bpj:jecome:v:11:y:2022:i:1:p:35-53:n:8. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.