IDEAS home Printed from https://ideas.repec.org/a/spr/testjl/v32y2023i1d10.1007_s11749-022-00838-7.html
   My bibliography  Save this article

Bandwidth selection for statistical matching and prediction

Author

Listed:
  • Inés Barbeito

    (Universidade da Coruña)

  • Ricardo Cao

    (Universidade da Coruña)

  • Stefan Sperlich

    (Université de Genève)

Abstract

While there exist many bandwidth selectors for estimation, bandwidth selection for statistical matching and prediction has hardly been studied so far. We introduce a computationally attractive selector for nonparametric out-of-sample prediction problems like data matching, impact evaluation, scenario simulations or imputing missings. Even though the method is bootstrap based, we can derive closed expressions for the criterion function which avoids the need of Monte Carlo approximations. We study both, asymptotic and finite sample performance. The derived consistency, convergence rate and extensive simulation studies show the successful operation of the selector. The method is illustrated by applying it to real data for studying the gender wage gap in Spain. Specifically, the salary of Spanish women is predicted nonparametrically by the wage equation estimated for men while conditioned on their own (i.e., women’s) characteristics. An important discrepancy between observed and predicted wages is found, exhibiting a serious gender wage gap.

Suggested Citation

  • Inés Barbeito & Ricardo Cao & Stefan Sperlich, 2023. "Bandwidth selection for statistical matching and prediction," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 418-446, March.
    • Handle: RePEc:spr:testjl:v:32:y:2023:i:1:d:10.1007_s11749-022-00838-7
    DOI: 10.1007/s11749-022-00838-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11749-022-00838-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11749-022-00838-7?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 search for a different version of it.

    References listed on IDEAS

    as
    1. repec:adr:anecst:y:2008:i:91-92:p:10 is not listed on IDEAS
    2. Ignacio Moral-Arce & Stefan Sperlich & Ana Fernández-Saínz & Maria Roca, 2012. "Trends in the Gender Pay Gap in Spain: A Semiparametric Analysis," Journal of Labor Research, Springer, vol. 33(2), pages 173-195, June.
    3. Max Köhler & Anja Schindler & Stefan Sperlich, 2014. "A Review and Comparison of Bandwidth Selection Methods for Kernel Regression," International Statistical Review, International Statistical Institute, vol. 82(2), pages 243-274, August.
    4. Jing Dai & Stefan Sperlich & Walter Zucchini, 2016. "A Simple Method for Predicting Distributions by Means of Covariates with Examples from Poverty and Health Economics," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 152(I), pages 49-80, March.
    5. Francine D. Blau & Lawrence M. Kahn, 2017. "The Gender Wage Gap: Extent, Trends, and Explanations," Journal of Economic Literature, American Economic Association, vol. 55(3), pages 789-865, September.
    6. Jose C. Galdo & Jeffrey Smith & Dan Black, 2008. "Bandwidth Selection and the Estimation of Treatment Effects with Unbalanced Data," Annals of Economics and Statistics, GENES, issue 91-92, pages 189-216.
    7. Jenny Häggström & Xavier Luna, 2014. "Targeted smoothing parameter selection for estimating average causal effects," Computational Statistics, Springer, vol. 29(6), pages 1727-1748, December.
    8. Rolf Tschernig & Lijian Yang, 2000. "Nonparametric Lag Selection for Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(4), pages 457-487, July.
    9. Nils-Bastian Heidenreich & Anja Schindler & Stefan Sperlich, 2013. "Bandwidth selection for kernel density estimation: a review of fully automatic selectors," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(4), pages 403-433, October.
    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. José María Sarabia & Faustino Prieto & Vanesa Jordá & Stefan Sperlich, 2020. "A Note on Combining Machine Learning with Statistical Modeling for Financial Data Analysis," Risks, MDPI, vol. 8(2), pages 1-14, April.
    2. Stefan Sperlich, 2022. "Comments on: hybrid semiparametric Bayesian networks," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 335-339, June.
    3. Iga Magda & Ewa Cukrowska-Torzewska, 2018. "Do female managers help to lower within-firm gender pay gaps? Public institutions vs. private enterprises," IBS Working Papers 08/2018, Instytut Badan Strukturalnych.
    4. Scholz, Michael & Nielsen, Jens Perch & Sperlich, Stefan, 2015. "Nonparametric prediction of stock returns based on yearly data: The long-term view," Insurance: Mathematics and Economics, Elsevier, vol. 65(C), pages 143-155.
    5. Joanna Tyrowicz & Lucas van der Velde, 2017. "When the opportunity knocks: large structural shocks and gender wage gaps," GRAPE Working Papers 2, GRAPE Group for Research in Applied Economics.
    6. Katrin Huber & Geske Rolvering, 2023. "Public child care and mothers’ career trajectories," Working Papers 228, Bavarian Graduate Program in Economics (BGPE).
    7. Amano-Patiño, N. & Baron, T. & Xiao, P., 2020. "Human Capital Accumulation, Equilibrium Wage-Setting and the Life-Cycle Gender Pay Gap," Cambridge Working Papers in Economics 2010, Faculty of Economics, University of Cambridge.
    8. Dreber, Anna & Heikensten, Emma & Säve-Söderbergh, Jenny, 2022. "Why do women ask for less?," Labour Economics, Elsevier, vol. 78(C).
    9. Genz, Sabrina & Schnabel, Claus, 2023. "Digitalization is not gender-neutral," Economics Letters, Elsevier, vol. 230(C).
    10. Rania Gihleb & Osnat Lifshitz, 2022. "Dynamic Effects of Educational Assortative Mating on Labor Supply," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 46, pages 302-327, October.
    11. Christian Pfeifer & Gesine Stephan, 2019. "Why women do not ask: gender differences in fairness perceptions of own wages and subsequent wage growth," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 43(2), pages 295-310.
    12. Kelley Sarussi & Thomas Walstrum, 2019. "Education and the Evolution of Earnings Across Population Groups Since 2000," Profitwise, Federal Reserve Bank of Chicago, issue 5, pages 1-13.
    13. Lorenzo Ductor & Sanjeev Goyal & Anja Prummer, 2018. "Gender & Collaboration," Working Papers 856, Queen Mary University of London, School of Economics and Finance.
    14. Maria Kravtsova & Aleksey Oshchepkov, 2019. "Market And Network Corruption," HSE Working papers WP BRP 209/EC/2019, National Research University Higher School of Economics.
    15. Benjamin Bennett & Isil Erel & Léa H. Stern & Zexi Wang, 2020. "Paid Leave Pays Off: The Effects of Paid Family Leave on Firm Performance," NBER Working Papers 27788, National Bureau of Economic Research, Inc.
    16. Katie Meara & Francesco Pastore & Allan Webster, 2020. "The gender pay gap in the USA: a matching study," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(1), pages 271-305, January.
    17. Kessel, Dany & Mollerstrom, Johanna & van Veldhuizen, Roel, 2021. "Can simple advice eliminate the gender gap in willingness to compete?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 138, pages 1-1.
    18. Asadullah, M. Niaz & Xiao, Saizi, 2020. "The changing pattern of wage returns to education in post-reform China," Structural Change and Economic Dynamics, Elsevier, vol. 53(C), pages 137-148.
    19. Cemal Eren Arbatlı & Quamrul H. Ashraf & Oded Galor & Marc Klemp, 2020. "Diversity and Conflict," Econometrica, Econometric Society, vol. 88(2), pages 727-797, March.
    20. Zhang, Cheng & Weng, Xiyan, 2024. "Can broadband infrastructure construction promote equality of opportunity? Evidence from a quasi-natural experiment in China☆," Journal of Asian Economics, Elsevier, vol. 93(C).

    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:spr:testjl:v:32:y:2023:i:1:d:10.1007_s11749-022-00838-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.