IDEAS home Printed from https://ideas.repec.org/h/eme/aecozz/s0731-90532019000040b009.html
   My bibliography  Save this book chapter

Estimation and Applications of Quantile Regression for Binary Longitudinal Data

In: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B

Author

Listed:
  • Mohammad Arshad Rahman
  • Angela Vossmeyer

Abstract

This chapter develops a framework for quantile regression in binary longitudinal data settings. A novel Markov chain Monte Carlo (MCMC) method is designed to fit the model and its computational efficiency is demonstrated in a simulation study. The proposed approach is flexible in that it can account for common and individual-specific parameters, as well as multivariate heterogeneity associated with several covariates. The methodology is applied to study female labor force participation and home ownership in the United States. The results offer new insights at the various quantiles, which are of interest to policymakers and researchers alike.

Suggested Citation

  • Mohammad Arshad Rahman & Angela Vossmeyer, 2019. "Estimation and Applications of Quantile Regression for Binary Longitudinal Data," Advances in Econometrics, in: Ivan Jeliazkov & Justin L. Tobias (ed.), Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B, volume 40, pages 157-191, Emerald Publishing Ltd.
    • Handle: RePEc:eme:aecozz:s0731-90532019000040b009
    as

    Download full text from publisher

    File URL: http://www.emeraldinsight.com/10.1108/S0731-90532019000040B009?utm_campaign=RePEc&WT.mc_id=RePEc
    Download Restriction: Access to full text is restricted to subscribers

    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. Francesco Bartolucci & Valentina Nigro, 2010. "A Dynamic Model for Binary Panel Data With Unobserved Heterogeneity Admitting a √n-Consistent Conditional Estimator," Econometrica, Econometric Society, vol. 78(2), pages 719-733, March.
    2. Harding, Matthew & Lamarche, Carlos, 2009. "A quantile regression approach for estimating panel data models using instrumental variables," Economics Letters, Elsevier, vol. 104(3), pages 133-135, September.
    3. Martin Burda & Matthew Harding, 2013. "Panel Probit With Flexible Correlated Effects: Quantifying Technology Spillovers In The Presence Of Latent Heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 956-981, September.
    4. Christian A. L. Hilber & Tracy M. Turner, 2014. "The Mortgage Interest Deduction and its Impact on Homeownership Decisions," The Review of Economics and Statistics, MIT Press, vol. 96(4), pages 618-637, October.
    5. Tracy M. Turner & Marc T. Smith, 2009. "Exits From Homeownership: The Effects Of Race, Ethnicity, And Income," Journal of Regional Science, Wiley Blackwell, vol. 49(1), pages 1-32, February.
    6. Bartolucci, Francesco & Farcomeni, Alessio, 2009. "A Multivariate Extension of the Dynamic Logit Model for Longitudinal Data Based on a Latent Markov Heterogeneity Structure," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 816-831.
    7. Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2007. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9780521671736, June.
    8. Ivan Jeliazkov & Angela Vossmeyer, 2018. "The impact of estimation uncertainty on covariate effects in nonlinear models," Statistical Papers, Springer, vol. 59(3), pages 1031-1042, September.
    9. Geoffrey Carliner, 1974. "Determinants of Home Ownership," Land Economics, University of Wisconsin Press, vol. 50(2), pages 109-119.
    10. Turner, Tracy M. & Luea, Heather, 2009. "Homeownership, wealth accumulation and income status," Journal of Housing Economics, Elsevier, vol. 18(2), pages 104-114, June.
    11. Rahim Alhamzawi & Haithem Taha Mohammad Ali, 2018. "Bayesian quantile regression for ordinal longitudinal data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(5), pages 815-828, April.
    12. Chan,Joshua & Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2019. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9781108423380, June.
    13. James J. Heckman & Thomas MaCurdy, 1982. "Corrigendum on A Life Cycle Model of Female Labour Supply," Review of Economic Studies, Oxford University Press, vol. 49(4), pages 659-660.
    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. Arjun Gupta & Soudeh Mirghasemi & Mohammad Arshad Rahman, 2020. "Heterogeneity in Food Expenditure amongst US families: Evidence from Longitudinal Quantile Regression," Papers 2010.02614, arXiv.org.

    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. Adnan Haider Bukhari & Safdar Ullah Khan, 2008. "A Small Open Economy DSGE Model for Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 47(4), pages 963-1008.
    2. Martinovici, A., 2019. "Revealing attention - how eye movements predict brand choice and moment of choice," Other publications TiSEM 7dca38a5-9f78-4aee-bd81-c, Tilburg University, School of Economics and Management.
    3. Hong Wang & Catherine S. Forbes & Jean-Pierre Fenech & John Vaz, 2018. "The determinants of bank loan recovery rates in good times and bad - new evidence," Papers 1804.07022, arXiv.org.
    4. Francesco Furlanetto & Francesco Ravazzolo & Samad Sarferaz, 2019. "Identification of Financial Factors in Economic Fluctuations," Economic Journal, Royal Economic Society, vol. 129(617), pages 311-337.
    5. Igari, Ryosuke & Hoshino, Takahiro, 2018. "A Bayesian data combination approach for repeated durations under unobserved missing indicators: Application to interpurchase-timing in marketing," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 150-166.
    6. Hasan, Iftekhar & Horvath, Roman & Mares, Jan, 2020. "Finance and wealth inequality," Journal of International Money and Finance, Elsevier, vol. 108(C).
    7. Obryan Poyser, 2017. "Exploring the determinants of Bitcoin's price: an application of Bayesian Structural Time Series," Papers 1706.01437, arXiv.org.
    8. Roman Horvath & Marek Rusnak & Katerina Smidkova & Jan Zapal, 2014. "The dissent voting behaviour of central bankers: what do we really know?," Applied Economics, Taylor & Francis Journals, vol. 46(4), pages 450-461, February.
    9. Bin Jiang & Anastasios Panagiotelis & George Athanasopoulos & Rob Hyndman & Farshid Vahid, 2016. "Bayesian Rank Selection in Multivariate Regression," Monash Econometrics and Business Statistics Working Papers 6/16, Monash University, Department of Econometrics and Business Statistics.
    10. Joshua C. C. Chan, 2018. "Specification tests for time-varying parameter models with stochastic volatility," Econometric Reviews, Taylor & Francis Journals, vol. 37(8), pages 807-823, September.
    11. Knut Are Aastveit & Gisle James Natvik & Sergio Sola, 2013. "Economic uncertainty and the effectiveness of monetary policy," Working Paper 2013/17, Norges Bank.
    12. Wang, Zheqi & Crook, Jonathan & Andreeva, Galina, 2020. "Reducing estimation risk using a Bayesian posterior distribution approach: Application to stress testing mortgage loan default," European Journal of Operational Research, Elsevier, vol. 287(2), pages 725-738.
    13. Franco Peracchi & Claudio Rossetti, 2019. "A Nonlinear Dynamic Factor Model of Health and Medical Treatment," CSEF Working Papers 524, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    14. Dahem, Ahlem, 2015. "Short term Bayesian inflation forecasting for Tunisia," MPRA Paper 66702, University Library of Munich, Germany.
    15. Chih-Sheng Hsieh & Michael König & Xiaodong Liu & Christian Zimmermann, 2020. "Collaboration in Bipartite Networks, with an Application to Coauthorship Networks," Tinbergen Institute Discussion Papers 20-056/VIII, Tinbergen Institute.
    16. Riley, Sarah F. & Ru, Hong Yu & Feng, Qing, 2013. "The User Cost of Low-Income Homeownership," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 43(2).
    17. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    18. Shinya Sugawara & Yasuhiro Omori, 2017. "An Econometric Analysis of Insurance Markets with Separate Identification for Moral Hazard and Selection Problems," Computational Economics, Springer;Society for Computational Economics, vol. 50(3), pages 473-502, October.
    19. Coulibaly, Issiaka & Gnimassoun, Blaise, 2013. "Optimality of a monetary union: New evidence from exchange rate misalignments in West Africa," Economic Modelling, Elsevier, vol. 32(C), pages 463-482.
    20. Colson, Gregory, 2009. "Improving nutrient content through genetic modification: Evidence from experimental auctions on consumer acceptance and willingness to pay for intragenic foods," ISU General Staff Papers 200901010800001872, Iowa State University, Department of Economics.

    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:eme:aecozz:s0731-90532019000040b009. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Charlotte Maiorana). General contact details of provider: http://www.emerald.com .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.