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David M. Kaplan

Not to be confused with: David Scott Kaplan

Personal Details

First Name:David
Middle Name:M.
Last Name:Kaplan
Suffix:
RePEc Short-ID:pka649
[This author has chosen not to make the email address public]
https://kaplandm.github.io
Terminal Degree:2013 Department of Economics; University of California-San Diego (UCSD) (from RePEc Genealogy)

Affiliation

Economics Department
University of Missouri

Columbia, Missouri (United States)
http://economics.missouri.edu/
RePEc:edi:edumous (more details at EDIRC)

Research output

as
Jump to: Working papers Articles Software

Working papers

  1. Wei Zhao & David M. Kaplan, 2023. "Conditions for Extrapolating Differences in Consumption to Differences in Welfare," Working Papers 2307, Department of Economics, University of Missouri.
  2. David M. Kaplan, 2023. "Smoothed instrumental variables quantile regression," Papers 2310.09013, arXiv.org.
  3. David M. Kaplan & Xin Liu, 2023. "Confidence Intervals for Intentionally Biased Estimators," Working Papers 2308, Department of Economics, University of Missouri.
  4. David M. Kaplan, 2021. "Distcomp: Comparing distributions," Papers 2110.02327, arXiv.org.
  5. David M. Kaplan & Xin Liu, 2021. "k-Class Instrumental Variables Quantile Regression," Working Papers 2104, Department of Economics, University of Missouri.
  6. David M. Kaplan, 2020. "sivqr: Smoothed IV quantile regression," Working Papers 2009, Department of Economics, University of Missouri.
  7. David M. Kaplan, 2020. "Assessing Policy Effects with Unconditional Quantile Regression," Working Papers 2011, Department of Economics, University of Missouri.
  8. David M. Kaplan, 2020. "Interpreting Unconditional Quantile Regression with Conditional Independence," Papers 2010.03606, arXiv.org, revised Oct 2021.
  9. David M. Kaplan, 2020. "Inference on Consensus Ranking of Distributions," Working Papers 2010, Department of Economics, University of Missouri.
  10. David M. Kaplan & Lonnie Hofmann, 2019. "High-order coverage of smoothed Bayesian bootstrap intervals for population quantiles," Working Papers 1914, Department of Economics, University of Missouri, revised 19 Sep 2020.
  11. David M. Kaplan, 2019. "Unbiased Estimation as a Public Good," Working Papers 1911, Department of Economics, University of Missouri.
  12. David M. Kaplan & Longhao Zhuo, 2018. "Comparing latent inequality with ordinal data," Working Papers 1816, Department of Economics, University of Missouri, revised Feb 2019.
  13. Matt Goldman & David M. Kaplan, 2017. "Comparing distributions by multiple testing across quantiles or CDF values," Papers 1708.04658, arXiv.org.
  14. Luciano de Castro & Antonio F. Galvao & David M. Kaplan, 2017. "Smoothed instrumental variables quantile regression, with estimation of quantile Euler equations," Working Papers 1710, Department of Economics, University of Missouri, revised 28 Feb 2018.
  15. Luciano de Castro & Antonio F. Galvao & David M. Kaplan & Xin Liu, 2017. "Smoothed GMM for quantile models," Papers 1707.03436, arXiv.org, revised Feb 2018.
  16. Matt Goldman & David M. Kaplan, 2016. "Fractional order statistic approximation for nonparametric conditional quantile inference," Papers 1609.09035, arXiv.org.
  17. David M. Kaplan & Longhao Zhuo, 2016. "Frequentist size of Bayesian inequality tests," Papers 1607.00393, arXiv.org, revised Feb 2018.
  18. David M. Kaplan & Yixiao Sun, 2016. "Smoothed estimating equations for instrumental variables quantile regression," Papers 1609.09033, arXiv.org.
  19. David M. Kaplan & Matt Goldman, 2015. "Nonparametric inference on conditional quantile differences and linear combinations, using L-statistics," Working Papers 1503, Department of Economics, University of Missouri.
  20. David M. Kaplan & Longhao Zhuo, 2015. "Bayesian and frequentist inequality tests," Working Papers 1516, Department of Economics, University of Missouri, revised Feb 2018.
  21. David M. Kaplan, 2014. "Nonparametric Inference on Quantile Marginal Effects," Working Papers 1413, Department of Economics, University of Missouri.
  22. David M. Kaplan & Matt Goldman, 2013. "IDEAL Quantile Inference via Interpolated Duals of Exact Analytic L-statistics," Working Papers 1315, Department of Economics, University of Missouri.
  23. David M. Kaplan, 2013. "IDEAL Inference on Conditional Quantiles via Interpolated Duals of Exact Analytic L-statistics," Working Papers 1316, Department of Economics, University of Missouri.
  24. David M. Kaplan & Matt Goldman, 2013. "Comparing distributions by multiple testing across quantiles," Working Papers 1319, Department of Economics, University of Missouri, revised Feb 2018.
  25. David M. Kaplan, 2013. "Improved Quantile Inference Via Fixed-Smoothing Asymptotics And Edgeworth Expansion," Working Papers 1313, Department of Economics, University of Missouri.
  26. Sun, Yixiao & Kaplan, David M., 2011. "A New Asymptotic Theory for Vector Autoregressive Long-run Variance Estimation and Autocorrelation Robust Testing," University of California at San Diego, Economics Working Paper Series qt8cx0t4gc, Department of Economics, UC San Diego.

Articles

  1. David M Kaplan & Wei Zhao, 2023. "Comparing latent inequality with ordinal data," The Econometrics Journal, Royal Economic Society, vol. 26(2), pages 189-214.
  2. David M. Kaplan, 2022. "Smoothed instrumental variables quantile regression," Stata Journal, StataCorp LP, vol. 22(2), pages 379-403, June.
  3. Kaplan, David M. & Zhuo, Longhao, 2021. "Frequentist properties of Bayesian inequality tests," Journal of Econometrics, Elsevier, vol. 221(1), pages 312-336.
  4. de Castro, Luciano & Galvao, Antonio F. & Kaplan, David M. & Liu, Xin, 2019. "Smoothed GMM for quantile models," Journal of Econometrics, Elsevier, vol. 213(1), pages 121-144.
  5. David M. Kaplan, 2019. "distcomp: Comparing distributions," Stata Journal, StataCorp LP, vol. 19(4), pages 832-848, December.
  6. Goldman, Matt & Kaplan, David M., 2018. "Comparing distributions by multiple testing across quantiles or CDF values," Journal of Econometrics, Elsevier, vol. 206(1), pages 143-166.
  7. Matt Goldman & David M. Kaplan, 2018. "Non‐parametric inference on (conditional) quantile differences and interquantile ranges, using L‐statistics," Econometrics Journal, Royal Economic Society, vol. 21(2), pages 136-169, June.
  8. Goldman, Matt & Kaplan, David M., 2017. "Fractional order statistic approximation for nonparametric conditional quantile inference," Journal of Econometrics, Elsevier, vol. 196(2), pages 331-346.
  9. Kaplan, David M. & Sun, Yixiao, 2017. "Smoothed Estimating Equations For Instrumental Variables Quantile Regression," Econometric Theory, Cambridge University Press, vol. 33(1), pages 105-157, February.
  10. Kaplan, David M., 2015. "Improved quantile inference via fixed-smoothing asymptotics and Edgeworth expansion," Journal of Econometrics, Elsevier, vol. 185(1), pages 20-32.

Software components

  1. David M. Kaplan, 2021. "DISTCOMP: Stata module to compare distributions," Statistical Software Components S459001, Boston College Department of Economics.
  2. David M. Kaplan, 2021. "SIVQR: Stata module to perform smoothed IV quantile regression," Statistical Software Components S459002, Boston College Department of Economics, revised 14 Mar 2023.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. David M. Kaplan, 2023. "Smoothed instrumental variables quantile regression," Papers 2310.09013, arXiv.org.

    Cited by:

    1. Sodokin, Koffi & Djafon, Joseph Kokouvi & Dandonougbo, Yevessé & Akakpo, Afi & Couchoro, Mawuli K. & Agbodji, Akoété Ega, 2023. "Technological change, completeness of financing microstructures, and impact on well-being and income inequality," Telecommunications Policy, Elsevier, vol. 47(6).
    2. Heboyan, Vahé & Hovhannisyan, Vardges & Bakhtavoryan, Rafael, 2023. "A Comprehensive Analysis of Tobacco Control Policies within a Smoothed Instrumental Variables Quantile Regression Framework," 2023 Annual Meeting, July 23-25, Washington D.C. 335614, Agricultural and Applied Economics Association.

  2. David M. Kaplan, 2021. "Distcomp: Comparing distributions," Papers 2110.02327, arXiv.org.

    Cited by:

    1. Fetene, Gebeyehu Manie & Balew, Solomon & Abro, Zewdu & Kassie, Menale & Tefera, Tadele, 2021. "Push-Pull Technology As a Climate-Smart Integrated Pest Management Strategy in Southern Ethiopia," 2021 Conference, August 17-31, 2021, Virtual 315246, International Association of Agricultural Economists.
    2. Ayllón, Sara, 2022. "Online teaching and gender bias," Economics of Education Review, Elsevier, vol. 89(C).
    3. David M. Kaplan, 2020. "Inference on Consensus Ranking of Distributions," Working Papers 2010, Department of Economics, University of Missouri.
    4. Heyman, Fredrik & Norbäck, Pehr-Johan & Persson, Lars, 2017. "Talent, Career Choice and Competition: The Gender Wage Gap at the Top," Working Paper Series 1169, Research Institute of Industrial Economics, revised 06 Mar 2023.
    5. Zewdu Abro & Gebeyehu Manie Fetene & Menale Kassie & Tigist Mekonnen Melesse, 2023. "Socioeconomic burden of trypanosomiasis: Evidence from crop and livestock production in Ethiopia," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(3), pages 785-799, September.
    6. Stefano Boscolo, 2019. "Quantifying the Redistributive Effect of the Erosion of the Italian Personal Income Tax Base: A Microsimulation Exercise," ECONOMIA PUBBLICA, FrancoAngeli Editore, vol. 2019(2), pages 39-80.

  3. David M. Kaplan, 2020. "sivqr: Smoothed IV quantile regression," Working Papers 2009, Department of Economics, University of Missouri.

    Cited by:

    1. Federico Favata & Sofia Zamparo, 2021. "Estimación del efecto de la segregación ocupacional por sexo en el ingreso laboral para Argentina (2016-2020)," Asociación Argentina de Economía Política: Working Papers 4467, Asociación Argentina de Economía Política.
    2. Feito-Ruiz, Isabel & Menéndez-Requejo, Susana, 2022. "Debt maturity in family firms: Heterogeneity across countries," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    3. Alfonso Rosolia, 2021. "Does information about current inflation affect expectations and decisions? Another look at Italian firms," Temi di discussione (Economic working papers) 1353, Bank of Italy, Economic Research and International Relations Area.

  4. David M. Kaplan, 2020. "Assessing Policy Effects with Unconditional Quantile Regression," Working Papers 2011, Department of Economics, University of Missouri.

    Cited by:

    1. Julian Martinez-Iriarte & Yixiao Sun, 2020. "Identification and Estimation of Unconditional Policy Effects of an Endogenous Binary Treatment: an Unconditional MTE Approach," Papers 2010.15864, arXiv.org, revised Jul 2023.
    2. Martínez-Iriarte, Julian & Sun, Yixiao, 2021. "Identification and Estimation of Unconditional Policy Effects of an Endogenous Binary Treatment: an Unconditional MTE Approach," University of California at San Diego, Economics Working Paper Series qt2bc57830, Department of Economics, UC San Diego.

  5. David M. Kaplan, 2020. "Interpreting Unconditional Quantile Regression with Conditional Independence," Papers 2010.03606, arXiv.org, revised Oct 2021.

    Cited by:

    1. Julian Martinez-Iriarte & Yixiao Sun, 2020. "Identification and Estimation of Unconditional Policy Effects of an Endogenous Binary Treatment: an Unconditional MTE Approach," Papers 2010.15864, arXiv.org, revised Jul 2023.
    2. Martínez-Iriarte, Julian & Sun, Yixiao, 2021. "Identification and Estimation of Unconditional Policy Effects of an Endogenous Binary Treatment: an Unconditional MTE Approach," University of California at San Diego, Economics Working Paper Series qt2bc57830, Department of Economics, UC San Diego.

  6. David M. Kaplan & Longhao Zhuo, 2018. "Comparing latent inequality with ordinal data," Working Papers 1816, Department of Economics, University of Missouri, revised Feb 2019.

    Cited by:

    1. Stephen P. Jenkins, 2020. "Comparing distributions of ordinal data," Stata Journal, StataCorp LP, vol. 20(3), pages 505-531, September.
    2. Grimes, Arthur & Jenkins, Stephen P. & Tranquilli, Florencia, 2020. "The Relationship between Subjective Wellbeing and Subjective Wellbeing Inequality: Taking Ordinality and Skewness Seriously," IZA Discussion Papers 13692, Institute of Labor Economics (IZA).
    3. Kaplan, David M. & Zhuo, Longhao, 2021. "Frequentist properties of Bayesian inequality tests," Journal of Econometrics, Elsevier, vol. 221(1), pages 312-336.
    4. Shuo Liu & Nick Netzer, 2023. "Happy Times: Measuring Happiness Using Response Times," CESifo Working Paper Series 10360, CESifo.
    5. David M. Kaplan & Longhao Zhuo, 2018. "Frequentist size of Bayesian inequality tests," Working Papers 1802, Department of Economics, University of Missouri, revised 14 Jul 2019.
    6. Arthur Grimes & Stephen P. Jenkins & Florencia Tranquilli, 2023. "The Relationship Between Subjective Wellbeing and Subjective Wellbeing Inequality: An Important Role for Skewness," Journal of Happiness Studies, Springer, vol. 24(1), pages 309-330, January.
    7. Andrew Chesher & Adam Rosen & Zahra Siddique, 2019. "Estimating Endogenous Effects on Ordinal Outcomes," CeMMAP working papers CWP66/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  7. Matt Goldman & David M. Kaplan, 2017. "Comparing distributions by multiple testing across quantiles or CDF values," Papers 1708.04658, arXiv.org.

    Cited by:

    1. John Mullahy, 2020. "Discovering Treatment Effectiveness via Median Treatment Effects—Applications to COVID-19 Clinical Trials," NBER Working Papers 27895, National Bureau of Economic Research, Inc.
    2. Gedikli, Cigdem & Popli, Gurleen & Yilmaz, Okan, 2023. "The impact of intimate partner violence on women’s labour market outcomes," World Development, Elsevier, vol. 164(C).
    3. David M. Kaplan & Matt Goldman, 2015. "Nonparametric inference on conditional quantile differences and linear combinations, using L-statistics," Working Papers 1503, Department of Economics, University of Missouri.
    4. Fasianos, Apostolos & Evgenidis, Anastasios, 2020. "Unconventional Monetary Policy and Wealth Inequalities in Great Britain," CEPR Discussion Papers 14656, C.E.P.R. Discussion Papers.
    5. David M. Kaplan, 2018. "distcomp: Comparing distributions," Working Papers 1817, Department of Economics, University of Missouri.
    6. David M. Kaplan & Longhao Zhuo, 2018. "Frequentist size of Bayesian inequality tests," Working Papers 1802, Department of Economics, University of Missouri, revised 14 Jul 2019.
    7. Goldman, Matt & Kaplan, David M., 2017. "Fractional order statistic approximation for nonparametric conditional quantile inference," Journal of Econometrics, Elsevier, vol. 196(2), pages 331-346.
    8. Huang, Wei & Li, Teng & Pan, Yinghao & Ren, Jinyang, 2021. "Teacher Characteristics and Student Performance: Evidence from Random Teacher-Student Assignments in China," IZA Discussion Papers 14184, Institute of Labor Economics (IZA).
    9. David M. Kaplan, 2020. "Inference on Consensus Ranking of Distributions," Working Papers 2010, Department of Economics, University of Missouri.
    10. Comin, Diego & Cirera, Xavi & Cruz, Marcio & Lee, Kyung Min, 2020. "Anatomy of Technology in the Firm," CEPR Discussion Papers 15427, C.E.P.R. Discussion Papers.
    11. Chung, EunYi & Olivares, Mauricio, 2021. "Permutation test for heterogeneous treatment effects with a nuisance parameter," Journal of Econometrics, Elsevier, vol. 225(2), pages 148-174.
    12. Dennis Wesselbaum, 2023. "Understanding the Drivers of the Gender Productivity Gap in the Economics Profession," The American Economist, Sage Publications, vol. 68(1), pages 61-73, March.
    13. Heyman, Fredrik & Norbäck, Pehr-Johan & Persson, Lars, 2017. "Talent, Career Choice and Competition: The Gender Wage Gap at the Top," Working Paper Series 1169, Research Institute of Industrial Economics, revised 06 Mar 2023.
    14. Cirera,Xavier & Comin,Diego Adolfo & Vargas Da Cruz,Marcio Jose & Lee,Kyungmin, 2020. "Technology Within and Across Firms," Policy Research Working Paper Series 9476, The World Bank.
    15. Wang, Duoyu & Cleary, Rebecca, 2023. "What contributes to the gap in nutritional quality across food security status?," 2023 Annual Meeting, July 23-25, Washington D.C. 335552, Agricultural and Applied Economics Association.
    16. Klenio Barbosa & Dakshina De Silva & Liyu Yang & Hisayuki Yoshimoto, 2020. "Bond Losses and Systemic Risk," Working Papers 288072615, Lancaster University Management School, Economics Department.
    17. Martin DeLuca & Roberto Pinheiro, 2023. "US Labor Market after COVID-19: An Interim Report," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2023(04), pages 1-7, February.
    18. John Mullahy, 2021. "Discovering treatment effectiveness via median treatment effects—Applications to COVID‐19 clinical trials," Health Economics, John Wiley & Sons, Ltd., vol. 30(5), pages 1050-1069, May.

  8. Luciano de Castro & Antonio F. Galvao & David M. Kaplan, 2017. "Smoothed instrumental variables quantile regression, with estimation of quantile Euler equations," Working Papers 1710, Department of Economics, University of Missouri, revised 28 Feb 2018.

    Cited by:

    1. Grigory Franguridi & Bulat Gafarov & Kaspar Wuthrich, 2020. "Bias correction for quantile regression estimators," Papers 2011.03073, arXiv.org, revised Dec 2022.
    2. Christophe Muller, 2019. "Linear Quantile Regression and Endogeneity Correction," Working Papers halshs-02272874, HAL.
    3. Xin Liu, 2019. "Averaging estimation for instrumental variables quantile regression," Papers 1910.04245, arXiv.org.
    4. Javier Alejo & Antonio F. Galvao & Gabriel Montes-Rojas, 2020. "A first-stage test for instrumental variables quantile regression," Asociación Argentina de Economía Política: Working Papers 4304, Asociación Argentina de Economía Política.
    5. David M. Kaplan, 2023. "Smoothed instrumental variables quantile regression," Papers 2310.09013, arXiv.org.
    6. Firpo, Sergio & Galvao, Antonio F. & Pinto, Cristine & Poirier, Alexandre & Sanroman, Graciela, 2022. "GMM quantile regression," Journal of Econometrics, Elsevier, vol. 230(2), pages 432-452.
    7. Hiroaki Kaido & Kaspar Wüthrich, 2018. "Decentralization estimators for instrumental variable quantile regression models," CeMMAP working papers CWP72/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Javier Alejo & Gabriel Montes-Rojas, 2021. "Quantile Regression under Limited Dependent Variable," Papers 2112.06822, arXiv.org.
    9. Koki Fusejima, 2020. "Identification of multi-valued treatment effects with unobserved heterogeneity," Papers 2010.04385, arXiv.org, revised Apr 2023.
    10. David Powell, 2022. "Quantile regression with nonadditive fixed effects," Empirical Economics, Springer, vol. 63(5), pages 2675-2691, November.
    11. He, Xuming & Pan, Xiaoou & Tan, Kean Ming & Zhou, Wen-Xin, 2023. "Smoothed quantile regression with large-scale inference," Journal of Econometrics, Elsevier, vol. 232(2), pages 367-388.
    12. de Castro, Luciano & Galvao, Antonio F. & Montes-Rojas, Gabriel, 2020. "Quantile selection in non-linear GMM quantile models," Economics Letters, Elsevier, vol. 195(C).
    13. Javier Alejo & Antonio F. Galvao & Gabriel Montes-Rojas, 2021. "A first-stage representation for instrumental variables quantile regression," Papers 2102.01212, arXiv.org, revised Feb 2022.

  9. Luciano de Castro & Antonio F. Galvao & David M. Kaplan & Xin Liu, 2017. "Smoothed GMM for quantile models," Papers 1707.03436, arXiv.org, revised Feb 2018.

    Cited by:

    1. Grigory Franguridi & Bulat Gafarov & Kaspar Wuthrich, 2020. "Bias correction for quantile regression estimators," Papers 2011.03073, arXiv.org, revised Dec 2022.
    2. Christophe Muller, 2019. "Linear Quantile Regression and Endogeneity Correction," Working Papers halshs-02272874, HAL.
    3. Xin Liu, 2019. "Averaging estimation for instrumental variables quantile regression," Papers 1910.04245, arXiv.org.
    4. Javier Alejo & Antonio F. Galvao & Gabriel Montes-Rojas, 2020. "A first-stage test for instrumental variables quantile regression," Asociación Argentina de Economía Política: Working Papers 4304, Asociación Argentina de Economía Política.
    5. David M. Kaplan, 2023. "Smoothed instrumental variables quantile regression," Papers 2310.09013, arXiv.org.
    6. Firpo, Sergio & Galvao, Antonio F. & Pinto, Cristine & Poirier, Alexandre & Sanroman, Graciela, 2022. "GMM quantile regression," Journal of Econometrics, Elsevier, vol. 230(2), pages 432-452.
    7. Hiroaki Kaido & Kaspar Wüthrich, 2018. "Decentralization estimators for instrumental variable quantile regression models," CeMMAP working papers CWP72/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Javier Alejo & Gabriel Montes-Rojas, 2021. "Quantile Regression under Limited Dependent Variable," Papers 2112.06822, arXiv.org.
    9. Koki Fusejima, 2020. "Identification of multi-valued treatment effects with unobserved heterogeneity," Papers 2010.04385, arXiv.org, revised Apr 2023.
    10. David Powell, 2022. "Quantile regression with nonadditive fixed effects," Empirical Economics, Springer, vol. 63(5), pages 2675-2691, November.
    11. He, Xuming & Pan, Xiaoou & Tan, Kean Ming & Zhou, Wen-Xin, 2023. "Smoothed quantile regression with large-scale inference," Journal of Econometrics, Elsevier, vol. 232(2), pages 367-388.
    12. de Castro, Luciano & Galvao, Antonio F. & Montes-Rojas, Gabriel, 2020. "Quantile selection in non-linear GMM quantile models," Economics Letters, Elsevier, vol. 195(C).
    13. Javier Alejo & Antonio F. Galvao & Gabriel Montes-Rojas, 2021. "A first-stage representation for instrumental variables quantile regression," Papers 2102.01212, arXiv.org, revised Feb 2022.

  10. Matt Goldman & David M. Kaplan, 2016. "Fractional order statistic approximation for nonparametric conditional quantile inference," Papers 1609.09035, arXiv.org.

    Cited by:

    1. Matt Goldman & David M. Kaplan, 2017. "Comparing distributions by multiple testing across quantiles or CDF values," Papers 1708.04658, arXiv.org.
    2. David M. Kaplan & Lonnie Hofmann, 2019. "High-order coverage of smoothed Bayesian bootstrap intervals for population quantiles," Working Papers 1914, Department of Economics, University of Missouri, revised 19 Sep 2020.
    3. David M. Kaplan & Matt Goldman, 2015. "Nonparametric inference on conditional quantile differences and linear combinations, using L-statistics," Working Papers 1503, Department of Economics, University of Missouri.
    4. David M. Kaplan, 2013. "Improved Quantile Inference Via Fixed-Smoothing Asymptotics And Edgeworth Expansion," Working Papers 1313, Department of Economics, University of Missouri.
    5. Alan Hutson, 2018. "Comment on “What Do Interpolated Nonparametric Confidence Intervals for Population Quantiles Guarantee?”, Frey and Zhang (2017)," The American Statistician, Taylor & Francis Journals, vol. 72(3), pages 302-302, July.
    6. David M. Kaplan & Matt Goldman, 2013. "Comparing distributions by multiple testing across quantiles," Working Papers 1319, Department of Economics, University of Missouri, revised Feb 2018.
    7. Chaitra H. Nagaraja & Haikady N. Nagaraja, 2020. "Distribution‐free Approximate Methods for Constructing Confidence Intervals for Quantiles," International Statistical Review, International Statistical Institute, vol. 88(1), pages 75-100, April.
    8. David M. Kaplan, 2014. "Nonparametric Inference on Quantile Marginal Effects," Working Papers 1413, Department of Economics, University of Missouri.

  11. David M. Kaplan & Longhao Zhuo, 2016. "Frequentist size of Bayesian inequality tests," Papers 1607.00393, arXiv.org, revised Feb 2018.

    Cited by:

    1. David M. Kaplan & Longhao Zhuo, 2019. "Comparing latent inequality with ordinal data," Working Papers 1909, Department of Economics, University of Missouri.
    2. Matt Goldman & David M. Kaplan, 2017. "Comparing distributions by multiple testing across quantiles or CDF values," Papers 1708.04658, arXiv.org.
    3. David M. Kaplan & Matt Goldman, 2013. "Comparing distributions by multiple testing across quantiles," Working Papers 1319, Department of Economics, University of Missouri, revised Feb 2018.

  12. David M. Kaplan & Yixiao Sun, 2016. "Smoothed estimating equations for instrumental variables quantile regression," Papers 1609.09033, arXiv.org.

    Cited by:

    1. Luciano de Castro & Antonio F. Galvao & David M. Kaplan & Xin Liu, 2017. "Smoothed GMM for quantile models," Papers 1707.03436, arXiv.org, revised Feb 2018.
    2. Fengrui Di & Lei Wang, 2022. "Multi-round smoothed composite quantile regression for distributed data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(5), pages 869-893, October.
    3. Le-Yu Chen & Sokbae (Simon) Lee, 2017. "Exact computation of GMM estimators for instrumental variable quantile regression models," CeMMAP working papers CWP52/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Grigory Franguridi & Bulat Gafarov & Kaspar Wuthrich, 2020. "Bias correction for quantile regression estimators," Papers 2011.03073, arXiv.org, revised Dec 2022.
    5. Santiago Pereda Fernández, 2019. "Identification and estimation of triangular models with a binary treatment," Temi di discussione (Economic working papers) 1210, Bank of Italy, Economic Research and International Relations Area.
    6. Federico Favata & Sofia Zamparo, 2021. "Estimación del efecto de la segregación ocupacional por sexo en el ingreso laboral para Argentina (2016-2020)," Asociación Argentina de Economía Política: Working Papers 4467, Asociación Argentina de Economía Política.
    7. Marcelo Fernandes & Emmanuel Guerre & Eduardo Horta, 2021. "Smoothing Quantile Regressions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 338-357, January.
    8. Sodokin, Koffi & Djafon, Joseph Kokouvi & Dandonougbo, Yevessé & Akakpo, Afi & Couchoro, Mawuli K. & Agbodji, Akoété Ega, 2023. "Technological change, completeness of financing microstructures, and impact on well-being and income inequality," Telecommunications Policy, Elsevier, vol. 47(6).
    9. Bruins, Marianne & Duffy, James A. & Keane, Michael P. & Smith, Anthony A., 2018. "Generalized indirect inference for discrete choice models," Journal of Econometrics, Elsevier, vol. 205(1), pages 177-203.
    10. Grigory Franguridi & Bulat Gafarov & Kaspar Wüthrich, 2021. "Conditional Quantile Estimators: A Small Sample Theory," CESifo Working Paper Series 9046, CESifo.
    11. Victor Chernozhukov & Christian Hansen & Kaspar Wuthrich, 2020. "Instrumental Variable Quantile Regression," Papers 2009.00436, arXiv.org.
    12. Yinchu Zhu, 2018. "Learning non-smooth models: instrumental variable quantile regressions and related problems," Papers 1805.06855, arXiv.org, revised Sep 2019.
    13. Xin Liu, 2019. "Averaging estimation for instrumental variables quantile regression," Papers 1910.04245, arXiv.org.
    14. David M. Kaplan, 2019. "Unbiased Estimation as a Public Good," Working Papers 1911, Department of Economics, University of Missouri.
    15. Idrissa Ouedraogo & Issa Dianda & Pegdwende Patrik Ouedraogo & Rodrigue Tiraogo Ouedraogo & Bassirou Konfe, 2022. "The effects of taxation on income inequality in sub-Saharan Africa," WIDER Working Paper Series wp-2022-129, World Institute for Development Economic Research (UNU-WIDER).
    16. Goldman, Matt & Kaplan, David M., 2017. "Fractional order statistic approximation for nonparametric conditional quantile inference," Journal of Econometrics, Elsevier, vol. 196(2), pages 331-346.
    17. David M. Kaplan, 2020. "Inference on Consensus Ranking of Distributions," Working Papers 2010, Department of Economics, University of Missouri.
    18. Borgen, Nicolai T. & Haupt, Andreas & Wiborg, Øyvind N., 2021. "Flexible and fast estimation of quantile treatment effects: The rqr and rqrplot commands," SocArXiv 4vquh, Center for Open Science.
    19. Su Liangjun & Tadao Hoshino, 2015. "Sieve Instrumental Variable Quantile Regression Estimation of Functional Coefficient Models," Working Papers 01-2015, Singapore Management University, School of Economics.
    20. David M. Kaplan, 2020. "sivqr: Smoothed IV quantile regression," Working Papers 2009, Department of Economics, University of Missouri.
    21. Javier Alejo & Antonio F. Galvao & Gabriel Montes-Rojas, 2020. "A first-stage test for instrumental variables quantile regression," Asociación Argentina de Economía Política: Working Papers 4304, Asociación Argentina de Economía Política.
    22. David M. Kaplan, 2023. "Smoothed instrumental variables quantile regression," Papers 2310.09013, arXiv.org.
    23. Wüthrich, Kaspar, 2019. "A closed-form estimator for quantile treatment effects with endogeneity," Journal of Econometrics, Elsevier, vol. 210(2), pages 219-235.
    24. Firpo, Sergio & Galvao, Antonio F. & Pinto, Cristine & Poirier, Alexandre & Sanroman, Graciela, 2022. "GMM quantile regression," Journal of Econometrics, Elsevier, vol. 230(2), pages 432-452.
    25. Kaspar Wüthrich, 2014. "A Comparison of two Quantile Models with Endogeneity," Diskussionsschriften dp1408, Universitaet Bern, Departement Volkswirtschaft.
    26. Hiroaki Kaido & Kaspar Wüthrich, 2018. "Decentralization estimators for instrumental variable quantile regression models," CeMMAP working papers CWP72/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    27. Jean-Jacques Forneron, 2023. "Noisy, Non-Smooth, Non-Convex Estimation of Moment Condition Models," Papers 2301.07196, arXiv.org, revised Feb 2023.
    28. Javier Alejo & Gabriel Montes-Rojas, 2021. "Quantile Regression under Limited Dependent Variable," Papers 2112.06822, arXiv.org.
    29. Lorenzo Tedesco & Jad Beyhum & Ingrid Van Keilegom, 2023. "Instrumental variable estimation of the proportional hazards model by presmoothing," Papers 2309.02183, arXiv.org.
    30. Kaspar Wüthrich, 2015. "Semiparametric estimation of quantile treatment effects with endogeneity," Diskussionsschriften dp1509, Universitaet Bern, Departement Volkswirtschaft.
    31. Machado, José A.F. & Santos Silva, J.M.C., 2019. "Quantiles via moments," Journal of Econometrics, Elsevier, vol. 213(1), pages 145-173.
    32. Luciano de Castro & Antonio F. Galvao & David M. Kaplan, 2017. "Smoothed instrumental variables quantile regression, with estimation of quantile Euler equations," Working Papers 1710, Department of Economics, University of Missouri, revised 28 Feb 2018.
    33. He, Xuming & Pan, Xiaoou & Tan, Kean Ming & Zhou, Wen-Xin, 2023. "Smoothed quantile regression with large-scale inference," Journal of Econometrics, Elsevier, vol. 232(2), pages 367-388.
    34. Huan, Meili & Dong, Fengxia, 2023. "Sustainable Agricultural Practices and Crop Yield in China’s Maize Production," 2023 Annual Meeting, July 23-25, Washington D.C. 335656, Agricultural and Applied Economics Association.
    35. Armstrong, Christopher S. & Blouin, Jennifer L. & Jagolinzer, Alan D. & Larcker, David F., 2015. "Corporate governance, incentives, and tax avoidance," Journal of Accounting and Economics, Elsevier, vol. 60(1), pages 1-17.
    36. Tae-Hwy Lee & Aman Ullah & He Wang, 2023. "The Second-order Bias and Mean Squared Error of Quantile Regression Estimators," Working Papers 202313, University of California at Riverside, Department of Economics.
    37. de Castro, Luciano & Galvao, Antonio F. & Montes-Rojas, Gabriel, 2020. "Quantile selection in non-linear GMM quantile models," Economics Letters, Elsevier, vol. 195(C).
    38. Javier Alejo & Antonio F. Galvao & Gabriel Montes-Rojas, 2021. "A first-stage representation for instrumental variables quantile regression," Papers 2102.01212, arXiv.org, revised Feb 2022.

  13. David M. Kaplan & Matt Goldman, 2015. "Nonparametric inference on conditional quantile differences and linear combinations, using L-statistics," Working Papers 1503, Department of Economics, University of Missouri.

    Cited by:

    1. Mototsugu Fukushige & Yingxin Shi, 2022. "Quantile regression approach for measuring production inefficiency with empirical application to the primary production sector for the Xinjiang Production and Construction Corps in China," Asia-Pacific Journal of Regional Science, Springer, vol. 6(2), pages 777-805, June.
    2. Matt Goldman & David M. Kaplan, 2017. "Comparing distributions by multiple testing across quantiles or CDF values," Papers 1708.04658, arXiv.org.
    3. David M. Kaplan & Lonnie Hofmann, 2019. "High-order coverage of smoothed Bayesian bootstrap intervals for population quantiles," Working Papers 1914, Department of Economics, University of Missouri, revised 19 Sep 2020.
    4. David M. Kaplan, 2013. "Improved Quantile Inference Via Fixed-Smoothing Asymptotics And Edgeworth Expansion," Working Papers 1313, Department of Economics, University of Missouri.
    5. Goldman, Matt & Kaplan, David M., 2017. "Fractional order statistic approximation for nonparametric conditional quantile inference," Journal of Econometrics, Elsevier, vol. 196(2), pages 331-346.
    6. David M. Kaplan & Matt Goldman, 2013. "Comparing distributions by multiple testing across quantiles," Working Papers 1319, Department of Economics, University of Missouri, revised Feb 2018.
    7. David M. Kaplan, 2014. "Nonparametric Inference on Quantile Marginal Effects," Working Papers 1413, Department of Economics, University of Missouri.

  14. David M. Kaplan & Longhao Zhuo, 2015. "Bayesian and frequentist inequality tests," Working Papers 1516, Department of Economics, University of Missouri, revised Feb 2018.

    Cited by:

    1. David M. Kaplan & Longhao Zhuo, 2019. "Comparing latent inequality with ordinal data," Working Papers 1909, Department of Economics, University of Missouri.
    2. Matt Goldman & David M. Kaplan, 2017. "Comparing distributions by multiple testing across quantiles or CDF values," Papers 1708.04658, arXiv.org.
    3. David M. Kaplan & Matt Goldman, 2013. "Comparing distributions by multiple testing across quantiles," Working Papers 1319, Department of Economics, University of Missouri, revised Feb 2018.

  15. David M. Kaplan, 2014. "Nonparametric Inference on Quantile Marginal Effects," Working Papers 1413, Department of Economics, University of Missouri.

    Cited by:

    1. David M. Kaplan & Matt Goldman, 2015. "Nonparametric inference on conditional quantile differences and linear combinations, using L-statistics," Working Papers 1503, Department of Economics, University of Missouri.
    2. Goldman, Matt & Kaplan, David M., 2017. "Fractional order statistic approximation for nonparametric conditional quantile inference," Journal of Econometrics, Elsevier, vol. 196(2), pages 331-346.

  16. David M. Kaplan & Matt Goldman, 2013. "IDEAL Quantile Inference via Interpolated Duals of Exact Analytic L-statistics," Working Papers 1315, Department of Economics, University of Missouri.

    Cited by:

    1. David M. Kaplan, 2013. "IDEAL Inference on Conditional Quantiles via Interpolated Duals of Exact Analytic L-statistics," Working Papers 1316, Department of Economics, University of Missouri.
    2. David M. Kaplan, 2013. "Improved Quantile Inference Via Fixed-Smoothing Asymptotics And Edgeworth Expansion," Working Papers 1313, Department of Economics, University of Missouri.
    3. David M. Kaplan & Matt Goldman, 2013. "Comparing distributions by multiple testing across quantiles," Working Papers 1319, Department of Economics, University of Missouri, revised Feb 2018.

  17. David M. Kaplan, 2013. "IDEAL Inference on Conditional Quantiles via Interpolated Duals of Exact Analytic L-statistics," Working Papers 1316, Department of Economics, University of Missouri.

    Cited by:

    1. Fan, Yanqin & Liu, Ruixuan, 2016. "A direct approach to inference in nonparametric and semiparametric quantile models," Journal of Econometrics, Elsevier, vol. 191(1), pages 196-216.

  18. David M. Kaplan & Matt Goldman, 2013. "Comparing distributions by multiple testing across quantiles," Working Papers 1319, Department of Economics, University of Missouri, revised Feb 2018.

    Cited by:

    1. John Mullahy, 2020. "Discovering Treatment Effectiveness via Median Treatment Effects—Applications to COVID-19 Clinical Trials," NBER Working Papers 27895, National Bureau of Economic Research, Inc.
    2. Gedikli, Cigdem & Popli, Gurleen & Yilmaz, Okan, 2023. "The impact of intimate partner violence on women’s labour market outcomes," World Development, Elsevier, vol. 164(C).
    3. David M. Kaplan & Matt Goldman, 2015. "Nonparametric inference on conditional quantile differences and linear combinations, using L-statistics," Working Papers 1503, Department of Economics, University of Missouri.
    4. Fasianos, Apostolos & Evgenidis, Anastasios, 2020. "Unconventional Monetary Policy and Wealth Inequalities in Great Britain," CEPR Discussion Papers 14656, C.E.P.R. Discussion Papers.
    5. David M. Kaplan, 2018. "distcomp: Comparing distributions," Working Papers 1817, Department of Economics, University of Missouri.
    6. David M. Kaplan & Longhao Zhuo, 2018. "Frequentist size of Bayesian inequality tests," Working Papers 1802, Department of Economics, University of Missouri, revised 14 Jul 2019.
    7. Goldman, Matt & Kaplan, David M., 2017. "Fractional order statistic approximation for nonparametric conditional quantile inference," Journal of Econometrics, Elsevier, vol. 196(2), pages 331-346.
    8. Huang, Wei & Li, Teng & Pan, Yinghao & Ren, Jinyang, 2021. "Teacher Characteristics and Student Performance: Evidence from Random Teacher-Student Assignments in China," IZA Discussion Papers 14184, Institute of Labor Economics (IZA).
    9. David M. Kaplan, 2020. "Inference on Consensus Ranking of Distributions," Working Papers 2010, Department of Economics, University of Missouri.
    10. Comin, Diego & Cirera, Xavi & Cruz, Marcio & Lee, Kyung Min, 2020. "Anatomy of Technology in the Firm," CEPR Discussion Papers 15427, C.E.P.R. Discussion Papers.
    11. Chung, EunYi & Olivares, Mauricio, 2021. "Permutation test for heterogeneous treatment effects with a nuisance parameter," Journal of Econometrics, Elsevier, vol. 225(2), pages 148-174.
    12. Dennis Wesselbaum, 2023. "Understanding the Drivers of the Gender Productivity Gap in the Economics Profession," The American Economist, Sage Publications, vol. 68(1), pages 61-73, March.
    13. Heyman, Fredrik & Norbäck, Pehr-Johan & Persson, Lars, 2017. "Talent, Career Choice and Competition: The Gender Wage Gap at the Top," Working Paper Series 1169, Research Institute of Industrial Economics, revised 06 Mar 2023.
    14. Cirera,Xavier & Comin,Diego Adolfo & Vargas Da Cruz,Marcio Jose & Lee,Kyungmin, 2020. "Technology Within and Across Firms," Policy Research Working Paper Series 9476, The World Bank.
    15. Wang, Duoyu & Cleary, Rebecca, 2023. "What contributes to the gap in nutritional quality across food security status?," 2023 Annual Meeting, July 23-25, Washington D.C. 335552, Agricultural and Applied Economics Association.
    16. Klenio Barbosa & Dakshina De Silva & Liyu Yang & Hisayuki Yoshimoto, 2020. "Bond Losses and Systemic Risk," Working Papers 288072615, Lancaster University Management School, Economics Department.
    17. Martin DeLuca & Roberto Pinheiro, 2023. "US Labor Market after COVID-19: An Interim Report," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2023(04), pages 1-7, February.
    18. John Mullahy, 2021. "Discovering treatment effectiveness via median treatment effects—Applications to COVID‐19 clinical trials," Health Economics, John Wiley & Sons, Ltd., vol. 30(5), pages 1050-1069, May.

  19. David M. Kaplan, 2013. "Improved Quantile Inference Via Fixed-Smoothing Asymptotics And Edgeworth Expansion," Working Papers 1313, Department of Economics, University of Missouri.

    Cited by:

    1. David M. Kaplan & Matt Goldman, 2015. "Nonparametric inference on conditional quantile differences and linear combinations, using L-statistics," Working Papers 1503, Department of Economics, University of Missouri.
    2. Goldman, Matt & Kaplan, David M., 2017. "Fractional order statistic approximation for nonparametric conditional quantile inference," Journal of Econometrics, Elsevier, vol. 196(2), pages 331-346.
    3. Dominique Guegan & Bertrand K. Hassani & Kehan Li, 2016. "A robust confidence interval of historical Value-at-Risk for small sample," Documents de travail du Centre d'Economie de la Sorbonne 16034, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    4. Chaitra H. Nagaraja & Haikady N. Nagaraja, 2020. "Distribution‐free Approximate Methods for Constructing Confidence Intervals for Quantiles," International Statistical Review, International Statistical Institute, vol. 88(1), pages 75-100, April.
    5. David M. Kaplan, 2014. "Nonparametric Inference on Quantile Marginal Effects," Working Papers 1413, Department of Economics, University of Missouri.
    6. Dominique Guegan & Bertrand Hassani & Kehan Li, 2017. "Measuring risks in the extreme tail: The extreme VaR and its confidence interval," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01317391, HAL.
    7. Dominique Guegan & Bertrand K. Hassani & Kehan Li, 2016. "Capturing the intrinsic uncertainty of the VaR: Spectrum representation of a saddlepoint approximation for an estimator of the VaR," Documents de travail du Centre d'Economie de la Sorbonne 16034r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Jul 2016.

  20. Sun, Yixiao & Kaplan, David M., 2011. "A New Asymptotic Theory for Vector Autoregressive Long-run Variance Estimation and Autocorrelation Robust Testing," University of California at San Diego, Economics Working Paper Series qt8cx0t4gc, Department of Economics, UC San Diego.

    Cited by:

    1. Kim, Min Seong & Sun, Yixiao, 2013. "Heteroskedasticity and spatiotemporal dependence robust inference for linear panel models with fixed effects," Journal of Econometrics, Elsevier, vol. 177(1), pages 85-108.
    2. Sun, Yixiao, 2013. "Fixed-smoothing Asymptotics in a Two-step GMM Framework," University of California at San Diego, Economics Working Paper Series qt64x4z265, Department of Economics, UC San Diego.
    3. David M. Kaplan, 2013. "Improved Quantile Inference Via Fixed-Smoothing Asymptotics And Edgeworth Expansion," Working Papers 1313, Department of Economics, University of Missouri.
    4. Yang, Jingjing & Vogelsang, Timothy J., 2018. "Finite sample performance of a long run variance estimator based on exactly (almost) unbiased autocovariance estimators," Economics Letters, Elsevier, vol. 165(C), pages 21-27.

Articles

  1. David M Kaplan & Wei Zhao, 2023. "Comparing latent inequality with ordinal data," The Econometrics Journal, Royal Economic Society, vol. 26(2), pages 189-214.
    See citations under working paper version above.
  2. David M. Kaplan, 2022. "Smoothed instrumental variables quantile regression," Stata Journal, StataCorp LP, vol. 22(2), pages 379-403, June.
    See citations under working paper version above.
  3. de Castro, Luciano & Galvao, Antonio F. & Kaplan, David M. & Liu, Xin, 2019. "Smoothed GMM for quantile models," Journal of Econometrics, Elsevier, vol. 213(1), pages 121-144.
    See citations under working paper version above.
  4. David M. Kaplan, 2019. "distcomp: Comparing distributions," Stata Journal, StataCorp LP, vol. 19(4), pages 832-848, December.
    See citations under working paper version above.
  5. Goldman, Matt & Kaplan, David M., 2018. "Comparing distributions by multiple testing across quantiles or CDF values," Journal of Econometrics, Elsevier, vol. 206(1), pages 143-166.
    See citations under working paper version above.
  6. Matt Goldman & David M. Kaplan, 2018. "Non‐parametric inference on (conditional) quantile differences and interquantile ranges, using L‐statistics," Econometrics Journal, Royal Economic Society, vol. 21(2), pages 136-169, June.
    See citations under working paper version above.
  7. Goldman, Matt & Kaplan, David M., 2017. "Fractional order statistic approximation for nonparametric conditional quantile inference," Journal of Econometrics, Elsevier, vol. 196(2), pages 331-346.
    See citations under working paper version above.
  8. Kaplan, David M. & Sun, Yixiao, 2017. "Smoothed Estimating Equations For Instrumental Variables Quantile Regression," Econometric Theory, Cambridge University Press, vol. 33(1), pages 105-157, February.
    See citations under working paper version above.
  9. Kaplan, David M., 2015. "Improved quantile inference via fixed-smoothing asymptotics and Edgeworth expansion," Journal of Econometrics, Elsevier, vol. 185(1), pages 20-32.
    See citations under working paper version above.Sorry, no citations of articles recorded.

Software components

    Sorry, no citations of software components recorded.

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 29 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (22) 2014-06-28 2014-06-28 2014-06-28 2014-06-28 2014-06-28 2014-09-05 2015-02-22 2015-02-28 2015-11-21 2017-07-23 2018-03-05 2018-03-26 2018-03-26 2018-12-17 2019-10-14 2019-10-21 2019-11-18 2020-10-19 2020-12-14 2020-12-14 2021-06-14 2023-07-10. Author is listed
  2. NEP-UPT: Utility Models & Prospect Theory (6) 2017-07-23 2018-03-05 2018-03-26 2020-12-14 2022-08-29 2023-07-10. Author is listed
  3. NEP-ORE: Operations Research (5) 2019-10-21 2020-12-14 2020-12-14 2020-12-14 2020-12-14. Author is listed
  4. NEP-HEA: Health Economics (2) 2018-12-17 2019-10-14
  5. NEP-ISF: Islamic Finance (1) 2021-08-30
  6. NEP-PBE: Public Economics (1) 2014-06-28
  7. NEP-RMG: Risk Management (1) 2020-10-19
  8. NEP-SEA: South East Asia (1) 2014-06-28

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