IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2502.04654.html
   My bibliography  Save this paper

A sliced Wasserstein and diffusion approach to random coefficient models

Author

Listed:
  • Keunwoo Lim
  • Ting Ye
  • Fang Han

Abstract

We propose a new minimum-distance estimator for linear random coefficient models. This estimator integrates the recently advanced sliced Wasserstein distance with the nearest neighbor methods, both of which enhance computational efficiency. We demonstrate that the proposed method is consistent in approximating the true distribution. Additionally, our formulation encourages a diffusion process-based algorithm, which holds independent interest and potential for broader applications.

Suggested Citation

  • Keunwoo Lim & Ting Ye & Fang Han, 2025. "A sliced Wasserstein and diffusion approach to random coefficient models," Papers 2502.04654, arXiv.org.
    • Handle: RePEc:arx:papers:2502.04654
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2502.04654
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rudolf Beran, 1993. "Semiparametric random coefficient regression models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 45(4), pages 639-654, December.
    2. Hoderlein, Stefan & Klemelä, Jussi & Mammen, Enno, 2010. "Analyzing The Random Coefficient Model Nonparametrically," Econometric Theory, Cambridge University Press, vol. 26(3), pages 804-837, June.
    3. Fox, Jeremy T. & Kim, Kyoo il & Yang, Chenyu, 2016. "A simple nonparametric approach to estimating the distribution of random coefficients in structural models," Journal of Econometrics, Elsevier, vol. 195(2), pages 236-254.
    4. Zhexiao Lin & Fang Han, 2024. "On the failure of the bootstrap for Chatterjee’s rank correlation," Biometrika, Biometrika Trust, vol. 111(3), pages 1063-1070.
    5. Arthur Lewbel & Krishna Pendakur, 2017. "Unobserved Preference Heterogeneity in Demand Using Generalized Random Coefficients," Journal of Political Economy, University of Chicago Press, vol. 125(4), pages 1100-1148.
    6. Zhexiao Lin & Peng Ding & Fang Han, 2023. "Estimation Based on Nearest Neighbor Matching: From Density Ratio to Average Treatment Effect," Econometrica, Econometric Society, vol. 91(6), pages 2187-2217, November.
    7. Jeremy T. Fox & Kyoo il Kim & Stephen P. Ryan & Patrick Bajari, 2011. "A simple estimator for the distribution of random coefficients," Quantitative Economics, Econometric Society, vol. 2(3), pages 381-418, November.
    8. Bonhomme, Stéphane & Denis, Angela, 2024. "Estimating heterogeneous effects: Applications to labor economics," Labour Economics, Elsevier, vol. 91(C).
    9. Zhen Miao & Weihao Kong & Ramya Korlakai Vinayak & Wei Sun & Fang Han, 2024. "Fisher-Pitman Permutation Tests Based on Nonparametric Poisson Mixtures with Application to Single Cell Genomics," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 119(545), pages 394-406, January.
    10. Fabian Dunker & Emil Mendoza & Marco Reale, 2025. "Regularized maximum likelihood estimation for the random coefficients model," Econometric Reviews, Taylor & Francis Journals, vol. 44(2), pages 192-213, February.
    11. Christophe Gaillac & Eric Gautier, 2021. "Nonparametric classes for identification in random coefficients models when regressors have limited variation," Working Papers hal-03231392, HAL.
    12. Heiss, Florian & Hetzenecker, Stephan & Osterhaus, Maximilian, 2022. "Nonparametric estimation of the random coefficients model: An elastic net approach," Journal of Econometrics, Elsevier, vol. 229(2), pages 299-321.
    Full references (including those not matched with items on IDEAS)

    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. Breunig, Christoph, 2021. "Varying random coefficient models," Journal of Econometrics, Elsevier, vol. 221(2), pages 381-408.
    2. Christoph Breunig, 2018. "Varying Random Coefficient Models," Papers 1804.03110, arXiv.org, revised Aug 2020.
    3. Escanciano, Juan Carlos, 2023. "Irregular identification of structural models with nonparametric unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 234(1), pages 106-127.
    4. Wang, Ao, 2023. "Sieve BLP: A semi-nonparametric model of demand for differentiated products," Journal of Econometrics, Elsevier, vol. 235(2), pages 325-351.
    5. Jackson Bunting, 2022. "Continuous permanent unobserved heterogeneity in dynamic discrete choice models," Papers 2202.03960, arXiv.org, revised Feb 2024.
    6. Hoderlein, Stefan & Holzmann, Hajo & Meister, Alexander, 2017. "The triangular model with random coefficients," Journal of Econometrics, Elsevier, vol. 201(1), pages 144-169.
    7. Florian Heiss & Stephan Hetzenecker & Maximilian Osterhaus, 2019. "Nonparametric Estimation of the Random Coefficients Model: An Elastic Net Approach," Papers 1909.08434, arXiv.org, revised Sep 2019.
    8. Crawford, Gregory S. & Griffith, Rachel & Iaria, Alessandro, 2021. "A survey of preference estimation with unobserved choice set heterogeneity," Journal of Econometrics, Elsevier, vol. 222(1), pages 4-43.
    9. Gao, Yichen & Li, Cong & Liang, Zhongwen, 2015. "Binary response correlated random coefficient panel data models," Journal of Econometrics, Elsevier, vol. 188(2), pages 421-434.
    10. Christoph Breunig & Stefan Hoderlein, 2016. "Nonparametric Specification Testing in Random Parameter Models," Boston College Working Papers in Economics 897, Boston College Department of Economics.
    11. Hyungsik Roger Moon & Matthew Shum & Martin Weidner, 2017. "Estimation of random coefficients logit demand models with interactive fixed effects," CeMMAP working papers 12/17, Institute for Fiscal Studies.
    12. Hyungsik Roger Roger Moon & Matthew Shum & Martin Weidner, 2012. "Estimation of random coefficients logit demand models with interactive fixed effects," CeMMAP working papers 08/12, Institute for Fiscal Studies.
    13. Christoph Breunig & Stefan Hoderlein, 2018. "Specification testing in random coefficient models," Quantitative Economics, Econometric Society, vol. 9(3), pages 1371-1417, November.
    14. Wesley Blundell & Gautam Gowrisankaran & Ashley Langer, 2020. "Escalation of Scrutiny: The Gains from Dynamic Enforcement of Environmental Regulations," American Economic Review, American Economic Association, vol. 110(8), pages 2558-2585, August.
    15. Gastón Illanes & Manisha Padi, 2019. "Retirement Policy and Annuity Market Equilibria: Evidence from Chile," NBER Working Papers 26285, National Bureau of Economic Research, Inc.
    16. Fabian Dunker & Konstantin Eckle & Katharina Proksch & Johannes Schmidt-Hieber, 2017. "Tests for qualitative features in the random coefficients model," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 225, Courant Research Centre PEG.
    17. Tadao Hoshino & Daichi Shimamoto & Yasuyuki Todo, 2020. "Accounting for Heterogeneity in Network Formation Behaviour: An Application to Vietnamese SMEs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(5), pages 1042-1067, October.
    18. Moon, Hyungsik Roger & Shum, Matthew & Weidner, Martin, 2018. "Estimation of random coefficients logit demand models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 206(2), pages 613-644.
    19. Ziming Lin & Fang Han, 2024. "On the consistency of bootstrap for matching estimators," Papers 2410.23525, arXiv.org, revised Nov 2024.
    20. Krishna Pendakur, 2018. "Welfare analysis when people are different," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 51(2), pages 321-360, May.

    More about this item

    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:arx:papers:2502.04654. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.