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Nonparametric estimation and inference about the overlap of two distributions


  • Anderson, Gordon
  • Linton, Oliver
  • Whang, Yoon-Jae


This paper develops methodology for nonparametric estimation of a measure of the overlap of two distributions based on kernel estimation techniques. This quantity has been proposed as a measure of economic polarization between two groups, Anderson (2004) and Anderson et al. (2010). In ecology it has been used to measure the overlap of species. We give the asymptotic distribution theory of our estimator, which in some cases of practical relevance is nonstandard due to a boundary value problem. We also propose a method for conducting inference based on estimation of unknown quantities in the limiting distribution and show that our method yields consistent inference in all cases we consider. We investigate the finite sample properties of our methods by simulation methods. We give an application to the study of polarization within China in recent years using household survey data from two provinces taken in 1987 and 2001. We find a big increase in polarization between 1987 and 2001 according to monetary outcomes but less change in terms of living space.

Suggested Citation

  • Anderson, Gordon & Linton, Oliver & Whang, Yoon-Jae, 2012. "Nonparametric estimation and inference about the overlap of two distributions," Journal of Econometrics, Elsevier, vol. 171(1), pages 1-23.
  • Handle: RePEc:eee:econom:v:171:y:2012:i:1:p:1-23 DOI: 10.1016/j.jeconom.2012.05.001

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    References listed on IDEAS

    1. Jean-Yves Duclos & Joan Esteban & Debraj Ray, 2004. "Polarization: Concepts, Measurement, Estimation," Econometrica, Econometric Society, vol. 72(6), pages 1737-1772, November.
    2. Oliver Linton & Esfandiar Maasoumi & Yoon-Jae Whang, 2005. "Consistent Testing for Stochastic Dominance under General Sampling Schemes," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 735-765.
    3. Wang, You-Qiang & Tsui, Kai-Yuen, 2000. " Polarization Orderings and New Classes of Polarization Indices," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 2(3), pages 349-363.
    4. Donald W. K. Andrews, 1999. "Estimation When a Parameter Is on a Boundary," Econometrica, Econometric Society, vol. 67(6), pages 1341-1384, November.
    5. Anderson, Gordon & Ge, Ying, 2005. "The size distribution of Chinese cities," Regional Science and Urban Economics, Elsevier, vol. 35(6), pages 756-776, November.
    6. Esteban, Joan & Ray, Debraj, 1994. "On the Measurement of Polarization," Econometrica, Econometric Society, vol. 62(4), pages 819-851, July.
    7. Hardle, W. & Park, B. U. & Tsybakov, A. B., 1995. "Estimation of Non-sharp Support Boundaries," Journal of Multivariate Analysis, Elsevier, vol. 55(2), pages 205-218, November.
    8. Gordon Anderson, 2010. "Polarization Of The Poor: Multivariate Relative Poverty Measurement Sans Frontiers," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 56(1), pages 84-101, March.
    9. Anderson, Gordon, 2004. "Toward an empirical analysis of polarization," Journal of Econometrics, Elsevier, vol. 122(1), pages 1-26, September.
    10. Victor Chernozhukov & Han Hong & Elie Tamer, 2007. "Estimation and Confidence Regions for Parameter Sets in Econometric Models," Econometrica, Econometric Society, vol. 75(5), pages 1243-1284, September.
    11. Gordon Anderson & Ying Ge & Teng Wah Leo, 2010. "Distributional Overlap: Simple, Multivariate, Parametric, and Nonparametric Tests for Alienation, Convergence, and General Distributional Difference Issues," Econometric Reviews, Taylor & Francis Journals, vol. 29(3), pages 247-275.
    12. Guido W. Imbens & Donald B. Rubin, 1997. "Estimating Outcome Distributions for Compliers in Instrumental Variables Models," Review of Economic Studies, Oxford University Press, vol. 64(4), pages 555-574.
    13. Clemons, Traci E. & Jr., Edwin L. Bradley, 2000. "A nonparametric measure of the overlapping coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 34(1), pages 51-61, July.
    14. Schmid, Friedrich & Schmidt, Axel, 2006. "Nonparametric estimation of the coefficient of overlapping--theory and empirical application," Computational Statistics & Data Analysis, Elsevier, vol. 50(6), pages 1583-1596, March.
    15. Chen, Song Xi, 1999. "Beta kernel estimators for density functions," Computational Statistics & Data Analysis, Elsevier, vol. 31(2), pages 131-145, August.
    16. Gordon Anderson & Ying Ge, 2009. "Intercity Income Inequality Growth and Convergence in China," Journal of Income Distribution, Journal of Income Distribution, vol. 18(1), pages 70-89, March.
    17. Holmström, Lasse & Klemelä, Jussi, 1992. "Asymptotic bounds for the expected L1 error of a multivariate kernel density estimator," Journal of Multivariate Analysis, Elsevier, vol. 42(2), pages 245-266, August.
    18. Donald W. K. Andrews & Patrik Guggenberger, 2009. "Hybrid and Size-Corrected Subsampling Methods," Econometrica, Econometric Society, vol. 77(3), pages 721-762, May.
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    Cited by:

    1. Lee, Sokbae & Song, Kyungchul & Whang, Yoon-Jae, 2013. "Testing functional inequalities," Journal of Econometrics, Elsevier, vol. 172(1), pages 14-32.
    2. Sokbae Lee & Kyungchul Song & Yoon-Jae Whang, 2014. "Testing For A General Class Of Functional Inequalities," KIER Working Papers 889, Kyoto University, Institute of Economic Research.
    3. Anderson, Gordon & Leo, Teng Wah, 2013. "An empirical examination of matching theories: The one child policy, partner choice and matching intensity in urban China," Journal of Comparative Economics, Elsevier, vol. 41(2), pages 468-489.
    4. Liu, Shen & Maharaj, Elizabeth Ann & Inder, Brett, 2014. "Polarization of forecast densities: A new approach to time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 345-361.
    5. Gordon Anderson & Teng Leo & Robert Muelhaupt, 2014. "Measuring Advances in Equality of Opportunity: The Changing Gender Gap in Educational Attainment in Canada in the Last Half Century," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 119(1), pages 73-99, October.
    6. beare, brendan & shi, xiaoxia, 2015. "An improved bootstrap test of density ratio ordering," MPRA Paper 74772, University Library of Munich, Germany.
    7. Wang, Dan & Tian, Lili, 2017. "Parametric methods for confidence interval estimation of overlap coefficients," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 12-26.
    8. François Gerard & Miikka Rokkanen & Christoph Rothe, 2016. "Bounds on Treatment Effects in Regression Discontinuity Designs under Manipulation of the Running Variable, with an Application to Unemployment Insurance in Brazil," NBER Working Papers 22892, National Bureau of Economic Research, Inc.
    9. Toru Kitagawa, 2013. "A bootstrap test for instrument validity in heterogeneous treatment effect models," CeMMAP working papers CWP53/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Dahl, Christian M. & Huber, Martin & Mellace, Giovanni, 2017. "It's never too LATE: A new look at local average treatment effects with or without defiers," Discussion Papers of Business and Economics 2/2017, University of Southern Denmark, Department of Business and Economics.
    11. Gordon Anderson & Maria Grazia Pittau & Roberto Zelli, 2016. "Assessing the convergence and mobility of nations without artificially specified class boundaries," Journal of Economic Growth, Springer, vol. 21(3), pages 283-304, September.
    12. Gordon Anderson & Maria Pittau & Roberto Zelli, 2014. "Poverty status probability: a new approach to measuring poverty and the progress of the poor," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 12(4), pages 469-488, December.
    13. Anderson, Gordon & Farcomeni, Alessio & Pittau, Maria Grazia & Zelli, Roberto, 2016. "A new approach to measuring and studying the characteristics of class membership: Examining poverty, inequality and polarization in urban China," Journal of Econometrics, Elsevier, vol. 191(2), pages 348-359.
    14. repec:spr:metron:v:75:y:2017:i:2:d:10.1007_s40300-017-0115-1 is not listed on IDEAS
    15. repec:spr:metron:v:75:y:2017:i:2:d:10.1007_s40300-017-0112-4 is not listed on IDEAS

    More about this item


    Kernel estimation; Inequality; Overlap coefficient; Poissonization; Total variation;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement


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