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Sukjin Han

Personal Details

First Name:Sukjin
Middle Name:
Last Name:Han
Suffix:
RePEc Short-ID:pha802
https://sites.google.com/site/sukjinhanwebpage/
Terminal Degree:2012 Economics Department; Yale University (from RePEc Genealogy)

Affiliation

Department of Economics
University of Texas-Austin

Austin, Texas (United States)
http://www.utexas.edu/cola/depts/economics/
RePEc:edi:deutxus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles Software

Working papers

  1. Sukjin Han & Shenshen Yang, 2020. "Sharp Bounds on Treatment Effects for Policy Evaluation," Papers 2009.13861, arXiv.org.
  2. Sukjin Han, 2019. "Optimal Dynamic Treatment Regimes and Partial Welfare Ordering," Papers 1912.10014, arXiv.org, revised Jul 2021.
  3. Victor Chernozhukov & Iván Fernández-Val & Sukjin Han & Amanda Kowalski, 2018. "Censored Quantile Instrumental Variable Estimation with Stata," NBER Working Papers 24232, National Bureau of Economic Research, Inc.
  4. Sukjin Han, 2018. "Identification in Nonparametric Models for Dynamic Treatment Effects," Papers 1805.09397, arXiv.org, revised Jan 2019.
  5. Jorge Balat & Sukjin Han, 2018. "Multiple Treatments with Strategic Interaction," Papers 1805.08275, arXiv.org, revised Sep 2019.
  6. Sukjin Han & Sungwon Lee, 2018. "Estimation in a Generalization of Bivariate Probit Models with Dummy Endogenous Regressors," Papers 1808.05792, arXiv.org, revised Mar 2019.
  7. Sukjin Han & Edward J. Vytlacil, 2013. "Identification in a Generalization of Bivariate Probit Models with Endogenous Regressors," Department of Economics Working Papers 130908, The University of Texas at Austin, Department of Economics.
  8. Sukjin Han, 2012. "Nonparametric Estimation of Triangular Simultaneous Equations Models under Weak Identification," Department of Economics Working Papers 140414, The University of Texas at Austin, Department of Economics, revised Apr 2014.
  9. Donald W.K. Andrews & Sukjin Han, 2008. "Invalidity of the Bootstrap and the m Out of n Bootstrap for Interval Endpoints Defined by Moment Inequalities," Cowles Foundation Discussion Papers 1671, Cowles Foundation for Research in Economics, Yale University.

Articles

  1. Sukjin Han, 2020. "Nonparametric estimation of triangular simultaneous equations models under weak identification," Quantitative Economics, Econometric Society, vol. 11(1), pages 161-202, January.
  2. Sukjin Han & Sungwon Lee, 2019. "Estimation in a generalization of bivariate probit models with dummy endogenous regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 994-1015, September.
  3. Victor Chernozhukov & Ivan Fernández-Val & Sukjin Han & Amanda Kowalski, 2019. "Censored quantile instrumental-variable estimation with Stata," Stata Journal, StataCorp LP, vol. 19(4), pages 768-781, December.
  4. Sukjin Han & Adam McCloskey, 2019. "Estimation and inference with a (nearly) singular Jacobian," Quantitative Economics, Econometric Society, vol. 10(3), pages 1019-1068, July.
  5. Han, Sukjin & Vytlacil, Edward J., 2017. "Identification in a generalization of bivariate probit models with dummy endogenous regressors," Journal of Econometrics, Elsevier, vol. 199(1), pages 63-73.
  6. Donald W. K. Andrews & Sukjin Han, 2009. "Invalidity of the bootstrap and the m out of n bootstrap for confidence interval endpoints defined by moment inequalities," Econometrics Journal, Royal Economic Society, vol. 12(s1), pages 172-199, January.

Software components

  1. Victor Chernozhukov & Ivan Fernandez-Val & Sukjin Han & Amanda Kowalski, 2012. "CQIV: Stata module to perform censored quantile instrumental variables regression," Statistical Software Components S457478, Boston College Department of Economics, revised 25 Sep 2019.

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. Victor Chernozhukov & Iván Fernández-Val & Sukjin Han & Amanda Kowalski, 2018. "Censored Quantile Instrumental Variable Estimation with Stata," NBER Working Papers 24232, National Bureau of Economic Research, Inc.

    Cited by:

    1. Kim, Young-Joo & Daly, Vincent, 2019. "The Education Gradient in Health: The Case of Obesity in the UK and US," Economics Discussion Papers 2019-4, School of Economics, Kingston University London.
    2. Abe Dunn, 2014. "Health Insurance and the Demand for Medical Care: Instrumental Variable Estimates Using Health Insurer Claims Data," BEA Working Papers 0107, Bureau of Economic Analysis.
    3. Sugimoto, Kota, 2021. "Ownership versus legal unbundling of electricity transmission network: Evidence from renewable energy investment in Germany," Energy Economics, Elsevier, vol. 99(C).
    4. David M. Kaplan, 2020. "sivqr: Smoothed IV quantile regression," Working Papers 2009, Department of Economics, University of Missouri.

  2. Sukjin Han, 2018. "Identification in Nonparametric Models for Dynamic Treatment Effects," Papers 1805.09397, arXiv.org, revised Jan 2019.

    Cited by:

    1. Juliano Assunção & Robert McMillan & Joshua Murphy & Eduardo Souza-Rodrigues, 2019. "Optimal Environmental Targeting in the Amazon Rainforest," NBER Working Papers 25636, National Bureau of Economic Research, Inc.
    2. Susan Athey & Guido Imbens, 2018. "Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption," Papers 1808.05293, arXiv.org, revised Sep 2018.
    3. Juliano Assuncao & Robert McMillan & Joshua Murphy & Eduardo Souza-Rodrigues, 2019. "Optimal Environmental Targeting in the Amazon Rainforest," Working Papers tecipa-631, University of Toronto, Department of Economics.

  3. Jorge Balat & Sukjin Han, 2018. "Multiple Treatments with Strategic Interaction," Papers 1805.08275, arXiv.org, revised Sep 2019.

    Cited by:

    1. Bora Kim, 2020. "Analysis of Randomized Experiments with Network Interference and Noncompliance," Papers 2012.13710, arXiv.org.
    2. Sukjin Han, 2018. "Identification in Nonparametric Models for Dynamic Treatment Effects," Papers 1805.09397, arXiv.org, revised Jan 2019.
    3. Tadao Hoshino & Takahide Yanagi, 2018. "Treatment Effect Models with Strategic Interaction in Treatment Decisions," Papers 1810.08350, arXiv.org, revised Jul 2021.
    4. Lin, Zhongjian & Vella, Francis, 2021. "Selection and Endogenous Treatment Models with Social Interactions: An Application to the Impact of Exercise on Self-Esteem," IZA Discussion Papers 14167, Institute of Labor Economics (IZA).

  4. Sukjin Han & Sungwon Lee, 2018. "Estimation in a Generalization of Bivariate Probit Models with Dummy Endogenous Regressors," Papers 1808.05792, arXiv.org, revised Mar 2019.

    Cited by:

    1. Mustafa Coban, 2021. "rbprobit: Recursive bivariate probit estimation and decomposition of marginal effects," London Stata Conference 2021 20, Stata Users Group.
    2. Manuel Denzer, 2019. "Estimating Causal Effects in Binary Response Models with Binary Endogenous Explanatory Variables - A Comparison of Possible Estimators," Working Papers 1916, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    3. Legendre, Nicolas & Nitani, Miwako & Riding, Allan, 2021. "Are franchises really more viable? Evidence from loan defaults," Journal of Business Research, Elsevier, vol. 133(C), pages 23-33.
    4. Acerenza, Santiago & Bartalotti, Otávio & Kedagni, Desire, 2021. "Testing Identifying Assumptions in Bivariate Probit Models," ISU General Staff Papers 202103290700001124, Iowa State University, Department of Economics.
    5. Jiaying Gu & Thomas M. Russell, 2021. "Partial Identification in Nonseparable Binary Response Models with Endogenous Regressors," Papers 2101.01254, arXiv.org, revised Jul 2021.

  5. Sukjin Han & Edward J. Vytlacil, 2013. "Identification in a Generalization of Bivariate Probit Models with Endogenous Regressors," Department of Economics Working Papers 130908, The University of Texas at Austin, Department of Economics.

    Cited by:

    1. Mourifié, Ismael & Méango, Romuald, 2014. "A note on the identification in two equations probit model with dummy endogenous regressor," Economics Letters, Elsevier, vol. 125(3), pages 360-363.
    2. Wheeler, Sarah Ann & Zuo, Alec, 2017. "The impact of drought and water scarcity on irrigator farm exit intentions in the Murray– Darling Basin," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 61(3), July.
    3. Sengupta, Reshmi & Rooj, Debasis, 2019. "The effect of health insurance on hospitalization: Identification of adverse selection, moral hazard and the vulnerable population in the Indian healthcare market," World Development, Elsevier, vol. 122(C), pages 110-129.

  6. Sukjin Han, 2012. "Nonparametric Estimation of Triangular Simultaneous Equations Models under Weak Identification," Department of Economics Working Papers 140414, The University of Texas at Austin, Department of Economics, revised Apr 2014.

    Cited by:

    1. Koo, Chao, 2018. "Essays on functional coefficient models," Other publications TiSEM ba87b8a5-3c55-40ec-967d-9, Tilburg University, School of Economics and Management.
    2. Edvard Bakhitov & Amandeep Singh, 2021. "Causal Gradient Boosting: Boosted Instrumental Variable Regression," Papers 2101.06078, arXiv.org.

Articles

  1. Sukjin Han, 2020. "Nonparametric estimation of triangular simultaneous equations models under weak identification," Quantitative Economics, Econometric Society, vol. 11(1), pages 161-202, January.
    See citations under working paper version above.
  2. Sukjin Han & Sungwon Lee, 2019. "Estimation in a generalization of bivariate probit models with dummy endogenous regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 994-1015, September.
    See citations under working paper version above.
  3. Victor Chernozhukov & Ivan Fernández-Val & Sukjin Han & Amanda Kowalski, 2019. "Censored quantile instrumental-variable estimation with Stata," Stata Journal, StataCorp LP, vol. 19(4), pages 768-781, December.
    See citations under working paper version above.
  4. Sukjin Han & Adam McCloskey, 2019. "Estimation and inference with a (nearly) singular Jacobian," Quantitative Economics, Econometric Society, vol. 10(3), pages 1019-1068, July.

    Cited by:

    1. Doko Tchatoka, Firmin & Wang, Wenjie, 2021. "Uniform Inference after Pretesting for Exogeneity with Heteroskedastic Data," MPRA Paper 106408, University Library of Munich, Germany.
    2. Firmin Doko Tchatoka & Wenjie Wang, 2020. "Uniform Inference after Pretesting for Exogeneity," School of Economics Working Papers 2020-05, University of Adelaide, School of Economics.
    3. Tetsuya Kaji, 2019. "Theory of Weak Identification in Semiparametric Models," Papers 1908.10478, arXiv.org, revised Aug 2020.
    4. Adam McCloskey, 2012. "Bonferroni-Based Size-Correction for Nonstandard Testing Problems," Working Papers 2012-16, Brown University, Department of Economics.
    5. Patrik Guggenberger & Frank Kleibergen & Sophocles Mavroeidis, 2021. "A Powerful Subvector Anderson Rubin Test in Linear Instrumental Variables Regression with Conditional Heteroskedasticity," Papers 2103.11371, arXiv.org, revised May 2021.
    6. Donald W.K. Andrews & Xu Cheng & Patrik Guggenberger, 2011. "Generic Results for Establishing the Asymptotic Size of Confidence Sets and Tests," Cowles Foundation Discussion Papers 1813, Cowles Foundation for Research in Economics, Yale University.
    7. Gregory Cox, 2018. "Almost Sure Uniqueness of a Global Minimum Without Convexity," Papers 1803.02415, arXiv.org, revised Feb 2019.
    8. Shakeeb Khan & Denis Nekipelov, 2019. "On Uniform Inference in Nonlinear Models with Endogeneity," Boston College Working Papers in Economics 986, Boston College Department of Economics.
    9. Frank Kleibergen & Zhaoguo Zhan, 2021. "Double robust inference for continuous updating GMM," Papers 2105.08345, arXiv.org.
    10. Valérie Lechene & Krishna Pendakur & Alexander Wolf, 2019. "OLS estimation of the intra-household distribution of consumption," IFS Working Papers W19/19, Institute for Fiscal Studies.
    11. Gregory Cox, 2020. "Weak Identification with Bounds in a Class of Minimum Distance Models," Papers 2012.11222, arXiv.org.

  5. Han, Sukjin & Vytlacil, Edward J., 2017. "Identification in a generalization of bivariate probit models with dummy endogenous regressors," Journal of Econometrics, Elsevier, vol. 199(1), pages 63-73.

    Cited by:

    1. Filippini, Massimo & Greene, William H. & Kumar, Nilkanth & Martinez-Cruz, Adan L., 2018. "A note on the different interpretation of the correlation parameters in the Bivariate Probit and the Recursive Bivariate Probit," Economics Letters, Elsevier, vol. 167(C), pages 104-107.
    2. David Zimmer, 2018. "Using copulas to estimate the coefficient of a binary endogenous regressor in a Poisson regression: Application to the effect of insurance on doctor visits," Health Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 545-556, March.
    3. Legendre, Nicolas & Nitani, Miwako & Riding, Allan, 2021. "Are franchises really more viable? Evidence from loan defaults," Journal of Business Research, Elsevier, vol. 133(C), pages 23-33.
    4. Bérengère Davin & Xavier Joutard & Alain Paraponaris, 2019. "“If You Were Me”: Proxy Respondents’ Biases in Population Health Surveys," AMSE Working Papers 1905, Aix-Marseille School of Economics, France.
    5. Acerenza, Santiago & Bartalotti, Otávio & Kedagni, Desire, 2021. "Testing Identifying Assumptions in Bivariate Probit Models," ISU General Staff Papers 202103290700001124, Iowa State University, Department of Economics.
    6. Martey, Edward, 2019. "Tenancy and energy choice for lighting and cooking: Evidence from Ghana," Energy Economics, Elsevier, vol. 80(C), pages 570-581.
    7. Li, Chuhui & Poskitt, D.S. & Zhao, Xueyan, 2019. "The bivariate probit model, maximum likelihood estimation, pseudo true parameters and partial identification," Journal of Econometrics, Elsevier, vol. 209(1), pages 94-113.
    8. Giampiero Marra & Rosalba Radice & David M. Zimmer, 2020. "Estimating the binary endogenous effect of insurance on doctor visits by copula‐based regression additive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 953-971, August.
    9. Massimo Filippini & Suchita Srinivasan, 2020. "Voluntary adoption of environmental standards and limited attention: Evidence from the food and beverage industry in Vietnam," CER-ETH Economics working paper series 20/338, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    10. Yanqin Fan & Marc Henry, 2020. "Vector copulas," Papers 2009.06558, arXiv.org, revised Apr 2021.
    11. Harris, Jeremiah & O'Brien, William, 2018. "U.S. worldwide taxation and domestic mergers and acquisitions," Journal of Accounting and Economics, Elsevier, vol. 66(2), pages 419-438.
    12. Tadao Hoshino & Takahide Yanagi, 2018. "Treatment Effect Models with Strategic Interaction in Treatment Decisions," Papers 1810.08350, arXiv.org, revised Jul 2021.
    13. Kim P. Huynh & Yuri Ostrovsky & Robert J. Petrunia & Marcel C. Voia, 2017. "Industry shutdown rates and permanent layoffs: evidence from firm-worker matched data," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 6(1), pages 1-31, December.
    14. Cohen, Jed & Azarova, Valeriya & Kollmann, Andrea & Reichl, Johannes, 2019. "Q-complementarity in household adoption of photovoltaics and electricity-intensive goods: The case of electric vehicles," Energy Economics, Elsevier, vol. 83(C), pages 567-577.
    15. Wang, Chunchao & Zhang, Chenglei & Ni, Jinlan & Zhang, Haifeng & Zhang, Junsen, 2019. "Family migration in China: Do migrant children affect parental settlement intention?," Journal of Comparative Economics, Elsevier, vol. 47(2), pages 416-428.
    16. Petri Böckerman & Pekka Ilmakunnas, 2017. "Do good working conditions make you work longer? Evidence on retirement decisions using linked survey and register data," Working Papers 315, Palkansaajien tutkimuslaitos, Labour Institute for Economic Research.
    17. Böckerman, Petri & Ilmakunnas, Pekka, 2020. "Do good working conditions make you work longer? Analyzing retirement decisions using linked survey and register data," The Journal of the Economics of Ageing, Elsevier, vol. 17(C).

  6. Donald W. K. Andrews & Sukjin Han, 2009. "Invalidity of the bootstrap and the m out of n bootstrap for confidence interval endpoints defined by moment inequalities," Econometrics Journal, Royal Economic Society, vol. 12(s1), pages 172-199, January.

    Cited by:

    1. Xavier d'Haultfoeuille & Roland Rathelot, 2011. "Measuring Segregation on Small Units : A Partial Identification Analysis," Working Papers 2011-18, Center for Research in Economics and Statistics.
    2. Federico A. Bugni & Ivan A. Canay & Xiaoxia Shi, 2014. "Inference for functions of partially identified parameters in moment inequality models," CeMMAP working papers CWP05/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Donald W.K. Andrews & Xiaoxia Shi, 2010. "Inference Based on Conditional Moment Inequalities," Cowles Foundation Discussion Papers 1761R2, Cowles Foundation for Research in Economics, Yale University, revised May 2012.
    4. Joel L. Horowitz, 2018. "Bootstrap Methods in Econometrics," Papers 1809.04016, arXiv.org.
    5. Ting Ye & Luke Keele & Raiden Hasegawa & Dylan S. Small, 2020. "A Negative Correlation Strategy for Bracketing in Difference-in-Differences," Papers 2006.02423, arXiv.org, revised Jan 2021.
    6. Joel L. Horowitz, 2018. "Bootstrap methods in econometrics," CeMMAP working papers CWP53/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

Software components

  1. Victor Chernozhukov & Ivan Fernandez-Val & Sukjin Han & Amanda Kowalski, 2012. "CQIV: Stata module to perform censored quantile instrumental variables regression," Statistical Software Components S457478, Boston College Department of Economics, revised 25 Sep 2019.

    Cited by:

    1. Martin Falk & Eva Hagsten, 2014. "Output growth and prices of establishments in the Swedish lodging industry," ERSA conference papers ersa14p360, European Regional Science Association.
    2. Gabrielle Fack & Camille Landais, 2013. "The effect of tax enforcement on tax elasticities: Evidence from charitable contributions in France," Economics Working Papers 1406, Department of Economics and Business, Universitat Pompeu Fabra.
    3. Mendolia, Silvia & Paloyo, Alfredo R. & Walker, Ian, 2016. "Heterogeneous effects of high school peers on educational outcomes," Ruhr Economic Papers 612, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    4. Melstrom, Richard T. & Jayasekera, Deshamithra H.W., 2016. "Two-Stage Estimation to Control for Unobservables in a Recreation Demand Model with Unvisited Sites," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236252, Agricultural and Applied Economics Association.
    5. Broberg, Thomas & Kazukauskas, Andrius, 2014. "Inefficiencies in residential use of energy - A critical overview of literature and energy efficiency policies in EU and Sweden," CERE Working Papers 2014:7, CERE - the Center for Environmental and Resource Economics.
    6. Sven Neelsen & Owen O'Donnell, 2017. "Progressive universalism? The impact of targeted coverage on health care access and expenditures in Peru," Health Economics, John Wiley & Sons, Ltd., vol. 26(12), pages 179-203, December.
    7. Kiran Krishnamurthy, Chandra & Kriström, Bengt, 2013. "Determinants of the price-premium for Green Energy: Evidence from an OECD cross-section," CERE Working Papers 2013:7, CERE - the Center for Environmental and Resource Economics, revised 30 Jun 2014.
    8. Michael J. Peel, 2014. "Addressing unobserved endogeneity bias in accounting studies: control and sensitivity methods by variable type," Accounting and Business Research, Taylor & Francis Journals, vol. 44(5), pages 545-571, October.

More information

Research fields, statistics, top rankings, if available.

Statistics

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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 6 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 (5) 2008-08-06 2013-10-05 2018-07-09 2018-09-03 2020-10-19. Author is listed
  2. NEP-DCM: Discrete Choice Models (1) 2018-09-03
  3. NEP-GTH: Game Theory (1) 2018-06-11
  4. NEP-KNM: Knowledge Management & Knowledge Economy (1) 2018-07-09
  5. NEP-TRE: Transport Economics (1) 2018-06-11

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