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Sung Jae Jun

Personal Details

First Name:Sung Jae
Middle Name:
Last Name:Jun
Suffix:
RePEc Short-ID:pju58
[This author has chosen not to make the email address public]
https://sites.google.com/view/suj14/
Terminal Degree:2006 Economics Department; Brown University (from RePEc Genealogy)

Affiliation

Department of Economics
Pennsylvania State University

State College, Pennsylvania (United States)
http://econ.la.psu.edu/
RePEc:edi:depsuus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles Software

Working papers

  1. Sung Jae Jun & Sokbae Lee, 2022. "Average Adjusted Association: Efficient Estimation with High Dimensional Confounders," Papers 2205.14048, arXiv.org, revised Apr 2023.
  2. Sung Jae Jun & Sokbae (Simon) Lee, 2020. "Causal inference in case-control studies," CeMMAP working papers CWP19/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  3. Sung Jae Jun & Sokbae Lee, 2020. "Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions," Papers 2004.08318, arXiv.org, revised Oct 2023.
  4. Sung Jae Jun & Sokbae Lee, 2018. "Identifying the Effect of Persuasion," Papers 1812.02276, arXiv.org, revised Nov 2022.
  5. Yoonseok Lee & Sung Jae Jun & Youngki Shin, 2014. "Treatment Effects with Unobserved Heterogeneity: A Set Identification Approach," Center for Policy Research Working Papers 169, Center for Policy Research, Maxwell School, Syracuse University.
  6. Sung Jae Jun & Joris Pinkse & Haiqing Xu & Nese Yildiz, 2012. "Identification of treatment effects in a triangular system of equations," Department of Economics Working Papers 130910, The University of Texas at Austin, Department of Economics, revised Oct 2012.
  7. Sung Jae Jun & Tony Lancaster, 2006. "Bayesian quantile regression," CeMMAP working papers CWP05/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

Articles

  1. Jun, Sung Jae & Pinkse, Joris, 2024. "An information–Theoretic approach to partially identified auction models," Journal of Econometrics, Elsevier, vol. 238(2).
  2. Sung Jae Jun & Sokbae Lee, 2024. "Causal Inference Under Outcome-Based Sampling with Monotonicity Assumptions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 998-1009, July.
  3. Sung Jae Jun & Sokbae Lee, 2023. "Identifying the Effect of Persuasion," Journal of Political Economy, University of Chicago Press, vol. 131(8), pages 2032-2058.
  4. Jun, Sung Jae & Zincenko, Federico, 2022. "Testing for risk aversion in first-price sealed-bid auctions," Journal of Econometrics, Elsevier, vol. 226(2), pages 295-320.
  5. Jun, Sung Jae & Pinkse, Joris, 2020. "Counterfactual prediction in complete information games: Point prediction under partial identification," Journal of Econometrics, Elsevier, vol. 216(2), pages 394-429.
  6. Jun, Sung Jae & Pinkse, Joris & Wan, Yuanyuan, 2017. "Integrated Score Estimation," Econometric Theory, Cambridge University Press, vol. 33(6), pages 1418-1456, December.
  7. Sung Jae Jun & Joris Pinkse & Haiqing Xu & Neşe Yıldız, 2016. "Multiple Discrete Endogenous Variables in Weakly-Separable Triangular Models," Econometrics, MDPI, vol. 4(1), pages 1-21, February.
  8. Sung Jae Jun & Joris Pinkse & Haiqing Xu, 2016. "Estimating a nonparametric triangular model with binary endogenous regressors," Econometrics Journal, Royal Economic Society, vol. 19(2), pages 113-149, June.
  9. Sung Jae Jun & Yoonseok Lee & Youngki Shin, 2016. "Treatment Effects With Unobserved Heterogeneity: A Set Identification Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 302-311, April.
  10. Jun, Sung Jae & Pinkse, Joris & Wan, Yuanyuan, 2015. "Classical Laplace estimation for n3-consistent estimators: Improved convergence rates and rate-adaptive inference," Journal of Econometrics, Elsevier, vol. 187(1), pages 201-216.
  11. Sung Jae Jun & Joris Pinkse & Haiqing Xu, 2012. "Discrete endogenous variables in weakly separable models," Econometrics Journal, Royal Economic Society, vol. 15(2), pages 288-303, June.
  12. Jun, Sung Jae & Pinkse, Joris, 2012. "Testing Under Weak Identification With Conditional Moment Restrictions," Econometric Theory, Cambridge University Press, vol. 28(6), pages 1229-1282, December.
  13. Jun, Sung Jae & Pinkse, Joris & Xu, Haiqing, 2011. "Tighter bounds in triangular systems," Journal of Econometrics, Elsevier, vol. 161(2), pages 122-128, April.
  14. Jun, Sung Jae & Pinkse, Joris & Wan, Yuanyuan, 2011. "-Consistent robust integration-based estimation," Journal of Multivariate Analysis, Elsevier, vol. 102(4), pages 828-846, April.
  15. Tony Lancaster & Sung Jae Jun, 2010. "Bayesian quantile regression methods," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 287-307.
  16. Jun, Sung Jae & Pinkse, Joris & Wan, Yuanyuan, 2010. "A consistent nonparametric test of affiliation in auction models," Journal of Econometrics, Elsevier, vol. 159(1), pages 46-54, November.
  17. Jun, Sung Jae & Pinkse, Joris, 2009. "Semiparametric tests of conditional moment restrictions under weak or partial identification," Journal of Econometrics, Elsevier, vol. 152(1), pages 3-18, September.
  18. Jun, Sung Jae & Pinkse, Joris, 2009. "Adding Regressors To Obtain Efficiency," Econometric Theory, Cambridge University Press, vol. 25(1), pages 298-301, February.
  19. Jun, Sung Jae, 2009. "Local structural quantile effects in a model with a nonseparable control variable," Journal of Econometrics, Elsevier, vol. 151(1), pages 82-97, July.
  20. Jun, Sung Jae & Pinkse, Joris, 2009. "Efficient Semiparametric Seemingly Unrelated Quantile Regression Estimation," Econometric Theory, Cambridge University Press, vol. 25(5), pages 1392-1414, October.
  21. Jun, Sung Jae, 2008. "Weak identification robust tests in an instrumental quantile model," Journal of Econometrics, Elsevier, vol. 144(1), pages 118-138, May.

Software components

  1. Sung Jae Jun & Sokbae Lee, 2021. "PERSUASIO: Stata module to estimate the effect of persuasion and conduct inference," Statistical Software Components S458902, Boston College Department of Economics, revised 24 Nov 2022.

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.

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Tony Lancaster & Sung Jae Jun, 2010. "Bayesian quantile regression methods," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 287-307.

    Mentioned in:

    1. Bayesian quantile regression methods (Journal of Applied Econometrics 2010) in ReplicationWiki ()

Working papers

  1. Sung Jae Jun & Sokbae Lee, 2020. "Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions," Papers 2004.08318, arXiv.org, revised Oct 2023.

    Cited by:

    1. Sung Jae Jun & Sokbae Lee, 2022. "Average Adjusted Association: Efficient Estimation with High Dimensional Confounders," Papers 2205.14048, arXiv.org, revised Apr 2023.

  2. Sung Jae Jun & Sokbae Lee, 2018. "Identifying the Effect of Persuasion," Papers 1812.02276, arXiv.org, revised Nov 2022.

    Cited by:

    1. Sung Jae Jun & Sokbae Lee, 2020. "Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions," Papers 2004.08318, arXiv.org, revised Oct 2023.
    2. Sung Jae Jun & Sokbae (Simon) Lee, 2020. "Causal inference in case-control studies," CeMMAP working papers CWP19/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Vincenzo Galasso & Massimo Morelli & Tommaso Nannicini & Piero Stanig, 2024. "The Populist Dynamic: Experimental Evidence on the Effects of Countering Populism," CESifo Working Paper Series 10949, CESifo.
    4. Wenlong Ji & Lihua Lei & Asher Spector, 2023. "Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects," Papers 2310.08115, arXiv.org.

  3. Yoonseok Lee & Sung Jae Jun & Youngki Shin, 2014. "Treatment Effects with Unobserved Heterogeneity: A Set Identification Approach," Center for Policy Research Working Papers 169, Center for Policy Research, Maxwell School, Syracuse University.

    Cited by:

    1. Brantly Callaway & Tong Li, 2019. "Quantile treatment effects in difference in differences models with panel data," Quantitative Economics, Econometric Society, vol. 10(4), pages 1579-1618, November.
    2. Khan, Shakeeb & Ponomareva, Maria & Tamer, Elie, 2011. "Identification of Panel Data Models with Endogenous Censoring," MPRA Paper 30373, University Library of Munich, Germany.
    3. Pablo Lavado & Gonzalo Rivera, 2016. "Identifying Treatment Effects with Data Combination and Unobserved Heterogeneity," Working Papers 79, Peruvian Economic Association.
    4. Shosei Sakaguchi, 2017. "Estimation of Average Treatment Effects Using Panel Data when Treatment Effects Are Heterogeneous by Unobserved Fixed Effects," KIER Working Papers 970, Kyoto University, Institute of Economic Research.
    5. Pablo Lavado & Gonzalo Rivera, 2015. "Identifying treatment effects and counterfactual distributions using data combination with unobserved heterogeneity," Working Papers 15-14, Centro de Investigación, Universidad del Pacífico.

  4. Sung Jae Jun & Tony Lancaster, 2006. "Bayesian quantile regression," CeMMAP working papers CWP05/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Giuseppe Ragusa, 2007. "Bayesian Likelihoods for Moment Condition Models," Working Papers 060714, University of California-Irvine, Department of Economics.
    2. Gareth W. Peters, 2018. "General Quantile Time Series Regressions for Applications in Population Demographics," Risks, MDPI, vol. 6(3), pages 1-47, September.

Articles

  1. Sung Jae Jun & Sokbae Lee, 2024. "Causal Inference Under Outcome-Based Sampling with Monotonicity Assumptions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 998-1009, July.
    See citations under working paper version above.
  2. Sung Jae Jun & Sokbae Lee, 2023. "Identifying the Effect of Persuasion," Journal of Political Economy, University of Chicago Press, vol. 131(8), pages 2032-2058.
    See citations under working paper version above.
  3. Jun, Sung Jae & Zincenko, Federico, 2022. "Testing for risk aversion in first-price sealed-bid auctions," Journal of Econometrics, Elsevier, vol. 226(2), pages 295-320.

    Cited by:

    1. Grundl, Serafin & Zhu, Yu, 2024. "Two results on auctions with endogenous entry," Economics Letters, Elsevier, vol. 234(C).
    2. Zincenko, Federico, 2024. "Estimation and inference of seller’s expected revenue in first-price auctions," Journal of Econometrics, Elsevier, vol. 241(1).

  4. Jun, Sung Jae & Pinkse, Joris, 2020. "Counterfactual prediction in complete information games: Point prediction under partial identification," Journal of Econometrics, Elsevier, vol. 216(2), pages 394-429.

    Cited by:

    1. Hoshino, Tadao & Yanagi, Takahide, 2023. "Treatment effect models with strategic interaction in treatment decisions," Journal of Econometrics, Elsevier, vol. 236(2).
    2. Jun, Sung Jae & Pinkse, Joris, 2024. "An information–Theoretic approach to partially identified auction models," Journal of Econometrics, Elsevier, vol. 238(2).

  5. Jun, Sung Jae & Pinkse, Joris & Wan, Yuanyuan, 2017. "Integrated Score Estimation," Econometric Theory, Cambridge University Press, vol. 33(6), pages 1418-1456, December.

    Cited by:

    1. Le-Yu Chen & Sokbae Lee, 2016. "Best Subset Binary Prediction," Papers 1610.02738, arXiv.org, revised May 2018.

  6. Sung Jae Jun & Joris Pinkse & Haiqing Xu & Neşe Yıldız, 2016. "Multiple Discrete Endogenous Variables in Weakly-Separable Triangular Models," Econometrics, MDPI, vol. 4(1), pages 1-21, February.

    Cited by:

    1. Francesco Longo & Karl Claxton & Stephen Martin & James Lomas, 2023. "More long‐term care for better healthcare and vice versa: investigating the mortality effects of interactions between these public sectors," Fiscal Studies, John Wiley & Sons, vol. 44(2), pages 189-216, June.
    2. Sukjin Han, 2018. "Identification in Nonparametric Models for Dynamic Treatment Effects," Papers 1805.09397, arXiv.org, revised Jan 2019.
    3. Christian Dippel & Robert Gold & Stephan Heblich & Rodrigo Pinto, 2017. "Instrumental Variables and Causal Mechanisms: Unpacking The Effect of Trade on Workers and Voters," NBER Working Papers 23209, National Bureau of Economic Research, Inc.
    4. Feng, Junlong, 2024. "Matching points: Supplementing instruments with covariates in triangular models," Journal of Econometrics, Elsevier, vol. 238(1).
    5. Songnian Chen & Shakeeb Khan & Xun Tang, 2022. "Endogeneity in Weakly Separable Models without Monotonicity," Papers 2208.05047, arXiv.org.
    6. Chen, Songnian & Khan, Shakeeb & Tang, Xun, 2024. "Endogeneity in weakly separable models without monotonicity," Journal of Econometrics, Elsevier, vol. 238(1).
    7. Shishir Shakya & Nabamita Dutta, 2024. "How Individualism Influences Female Financial Inclusion through Education: Evidence from Historical Prevalence of Infectious Diseases," Working Papers 24-03, Department of Economics, Appalachian State University.
    8. Shishir Shakya & Nabamita Dutta, 2024. "How Individualism Influences Female Financial Inclusion through Education: Evidence from Historical Prevalence of Infectious Diseases," Working Papers 24-07, Department of Economics, Appalachian State University.

  7. Sung Jae Jun & Joris Pinkse & Haiqing Xu, 2016. "Estimating a nonparametric triangular model with binary endogenous regressors," Econometrics Journal, Royal Economic Society, vol. 19(2), pages 113-149, June.

    Cited by:

    1. 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.

  8. Sung Jae Jun & Yoonseok Lee & Youngki Shin, 2016. "Treatment Effects With Unobserved Heterogeneity: A Set Identification Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 302-311, April.
    See citations under working paper version above.
  9. Jun, Sung Jae & Pinkse, Joris & Wan, Yuanyuan, 2015. "Classical Laplace estimation for n3-consistent estimators: Improved convergence rates and rate-adaptive inference," Journal of Econometrics, Elsevier, vol. 187(1), pages 201-216.

    Cited by:

    1. Alessandro Casini & Pierre Perron, 2018. "Generalized Laplace Inference in Multiple Change-Points Models," Papers 1803.10871, arXiv.org, revised Jan 2021.
    2. Le-Yu Chen & Sokbae Lee, 2016. "Best Subset Binary Prediction," Papers 1610.02738, arXiv.org, revised May 2018.
    3. Joris Pinkse & Karl Schurter, 2019. "Estimation of Auction Models with Shape Restrictions," Papers 1912.07466, arXiv.org.
    4. Sadat Reza & Paul Rilstone, 2019. "Smoothed Maximum Score Estimation of Discrete Duration Models," JRFM, MDPI, vol. 12(2), pages 1-16, April.

  10. Sung Jae Jun & Joris Pinkse & Haiqing Xu, 2012. "Discrete endogenous variables in weakly separable models," Econometrics Journal, Royal Economic Society, vol. 15(2), pages 288-303, June.

    Cited by:

    1. Jun, Sung Jae & Pinkse, Joris & Xu, Haiqing, 2011. "Tighter bounds in triangular systems," Journal of Econometrics, Elsevier, vol. 161(2), pages 122-128, April.
    2. Sungwon Lee, 2020. "Identification and Confidence Regions for Treatment Effect and its Distribution under Stochastic Dominance," Working Papers 2011, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    3. Feng, Junlong, 2024. "Matching points: Supplementing instruments with covariates in triangular models," Journal of Econometrics, Elsevier, vol. 238(1).
    4. Sung Jae Jun & Joris Pinkse & Haiqing Xu & Neşe Yıldız, 2016. "Multiple Discrete Endogenous Variables in Weakly-Separable Triangular Models," Econometrics, MDPI, vol. 4(1), pages 1-21, February.

  11. Jun, Sung Jae & Pinkse, Joris, 2012. "Testing Under Weak Identification With Conditional Moment Restrictions," Econometric Theory, Cambridge University Press, vol. 28(6), pages 1229-1282, December.

    Cited by:

    1. Antoine, Bertille & Lavergne, Pascal, 2023. "Identification-robust nonparametric inference in a linear IV model," Journal of Econometrics, Elsevier, vol. 235(1), pages 1-24.
    2. Kohtaro Hitomi & Masamune Iwasawa & Yoshihiko Nishiyama, 2020. "Optimal Minimax Rates against Non-smooth Alternatives," KIER Working Papers 1051, Kyoto University, Institute of Economic Research.
    3. Xu, Ruonan, 2021. "On the instrument functional form with a binary endogenous explanatory variable," Economics Letters, Elsevier, vol. 206(C).
    4. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    5. Zhipeng Liao & Xiaoxia Shi, 2020. "A nondegenerate Vuong test and post selection confidence intervals for semi/nonparametric models," Quantitative Economics, Econometric Society, vol. 11(3), pages 983-1017, July.
    6. Jinho Choi & Juan Carlos Escanciano & Junjie Guo, 2022. "Generalized band spectrum estimation with an application to the New Keynesian Phillips curve," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1055-1078, August.
    7. Bertille Antoine & Pascal Lavergne, 2020. "Identification-Robust Nonparametric Interference in a Linear IV Model," Discussion Papers dp20-03, Department of Economics, Simon Fraser University.
    8. Wang, Wenju & Wang, Qiao, 2019. "Consistent specification test for partially linear models with the k-nearest-neighbor method," Economics Letters, Elsevier, vol. 177(C), pages 89-93.
    9. Kohtaro Hitomi & Masamune Iwasawa & Yoshihiko Nishiyama, 2018. "Rate Optimal Specification Test When the Number of Instruments is Large," KIER Working Papers 986, Kyoto University, Institute of Economic Research.
    10. Antoine, Bertille & Lavergne, Pascal, 2014. "Conditional moment models under semi-strong identification," Journal of Econometrics, Elsevier, vol. 182(1), pages 59-69.
    11. Rachida Ouysse, 2014. "On the performance of block-bootstrap continuously updated GMM for a class of non-linear conditional moment models," Computational Statistics, Springer, vol. 29(1), pages 233-261, February.
    12. Li, Hongjun & Li, Qi & Liu, Ruixuan, 2016. "Consistent model specification tests based on k-nearest-neighbor estimation method," Journal of Econometrics, Elsevier, vol. 194(1), pages 187-202.

  12. Jun, Sung Jae & Pinkse, Joris & Xu, Haiqing, 2011. "Tighter bounds in triangular systems," Journal of Econometrics, Elsevier, vol. 161(2), pages 122-128, April.

    Cited by:

    1. Stefan Hoderlein & Yuya Sasaki, 2013. "Outcome Conditioned Treatment Effects," Boston College Working Papers in Economics 840, Boston College Department of Economics.
    2. Ismael Mourifie & Yuanyuan Wan, 2014. "Testing Local Average Treatment Effect Assumptions," Working Papers tecipa-514, University of Toronto, Department of Economics.
    3. Wooyoung Kim & Koohyun Kwon & Soonwoo Kwon & Sokbae Lee, 2018. "The identification power of smoothness assumptions in models with counterfactual outcomes," Quantitative Economics, Econometric Society, vol. 9(2), pages 617-642, July.
    4. 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.
    5. Alexander Torgovitsky, 2019. "Partial identification by extending subdistributions," Quantitative Economics, Econometric Society, vol. 10(1), pages 105-144, January.
    6. Sungwon Lee, 2020. "Identification and Confidence Regions for Treatment Effect and its Distribution under Stochastic Dominance," Working Papers 2011, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    7. Jun Ma & Vadim Marmer & Zhengfei Yu, 2021. "Inference on Individual Treatment Effects in Nonseparable Triangular Models," Papers 2107.05559, arXiv.org, revised Feb 2023.
    8. Kasy, Maximilian, "undated". "Instrumental variables with unrestricted heterogeneity and continuous treatment - DON'T CITE! SEE ERRATUM BELOW," Working Paper 33257, Harvard University OpenScholar.
    9. Balat, Jorge F. & Han, Sukjin, 2023. "Multiple treatments with strategic substitutes," Journal of Econometrics, Elsevier, vol. 234(2), pages 732-757.
    10. Rothe, Christoph, 2011. "Partial Distributional Policy Effects," IZA Discussion Papers 6076, Institute of Labor Economics (IZA).
    11. Feng, Junlong, 2024. "Matching points: Supplementing instruments with covariates in triangular models," Journal of Econometrics, Elsevier, vol. 238(1).
    12. Sung Jae Jun & Joris Pinkse & Haiqing Xu & Nese Yildiz, 2012. "Identification of treatment effects in a triangular system of equations," Department of Economics Working Papers 130910, The University of Texas at Austin, Department of Economics, revised Oct 2012.
    13. Liu, Nianqing & Vuong, Quang & Xu, Haiqing, 2017. "Rationalization and identification of binary games with correlated types," Journal of Econometrics, Elsevier, vol. 201(2), pages 249-268.
    14. Andrew Chesher & Adam Rosen, 2012. "Simultaneous equations for discrete outcomes: coherence, completeness, and identification," CeMMAP working papers 21/12, Institute for Fiscal Studies.
    15. Andrew Chesher & Konrad Smolinski, 2010. "Sharp identified sets for discrete variable IV models," CeMMAP working papers CWP11/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Desire Kedagni & Ismael Mourifie, 2014. "Tightening Bounds In Triangular Systems," Working Papers tecipa-515, University of Toronto, Department of Economics.
    17. Brendan Kline, 2016. "Identification of the Direction of a Causal Effect by Instrumental Variables," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 176-184, April.
    18. Lee, Jinhyun, 2013. "Sharp Bounds on Heterogeneous Individual Treatment Responses," SIRE Discussion Papers 2013-89, Scottish Institute for Research in Economics (SIRE).
    19. Maximilian Kasy, 2014. "Instrumental Variables with Unrestricted Heterogeneity and Continuous Treatment," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(4), pages 1614-1636.
    20. Sung Jae Jun & Joris Pinkse & Haiqing Xu & Neşe Yıldız, 2016. "Multiple Discrete Endogenous Variables in Weakly-Separable Triangular Models," Econometrics, MDPI, vol. 4(1), pages 1-21, February.

  13. Tony Lancaster & Sung Jae Jun, 2010. "Bayesian quantile regression methods," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 287-307.

    Cited by:

    1. Bernstein, David H. & Parmeter, Christopher F. & Tsionas, Mike G., 2023. "On the performance of the United States nuclear power sector: A Bayesian approach," Energy Economics, Elsevier, vol. 125(C).
    2. 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.
    3. Theodore Panagiotidis & Gianluigi Pelloni, 2014. "Asymmetry and Lilien’s Sectoral Shifts Hypothesis: A Quantile Regression Approach," Review of Economic Analysis, Digital Initiatives at the University of Waterloo Library, vol. 6(1), pages 68-86, June.
    4. Alhamzawi, Rahim & Yu, Keming, 2013. "Conjugate priors and variable selection for Bayesian quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 209-219.
    5. Wu Wang & Zhongyi Zhu, 2017. "Conditional empirical likelihood for quantile regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(1), pages 1-16, January.
    6. Korobilis, Dimitris, 2015. "Quantile forecasts of inflation under model uncertainty," MPRA Paper 64341, University Library of Munich, Germany.
    7. Philip Kostov, 2013. "Empirical likelihood estimation of the spatial quantile regression," Journal of Geographical Systems, Springer, vol. 15(1), pages 51-69, January.
    8. Dries Benoit & Rahim Alhamzawi & Keming Yu, 2013. "Bayesian lasso binary quantile regression," Computational Statistics, Springer, vol. 28(6), pages 2861-2873, December.
    9. Chang-Sheng Liu & Han-Ying Liang, 2023. "Bayesian empirical likelihood of quantile regression with missing observations," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(3), pages 285-313, April.
    10. Gareth W. Peters, 2018. "General Quantile Time Series Regressions for Applications in Population Demographics," Risks, MDPI, vol. 6(3), pages 1-47, September.
    11. Lane F. Burgette & Jerome P. Reiter, 2012. "Modeling Adverse Birth Outcomes via Confirmatory Factor Quantile Regression," Biometrics, The International Biometric Society, vol. 68(1), pages 92-100, March.
    12. Ando, Tomohiro & Bai, Jushan, 2018. "Quantile co-movement in financial markets: A panel quantile model with unobserved heterogeneity," MPRA Paper 88765, University Library of Munich, Germany.
    13. Ramsey, A., 2018. "Conditional Distributions of Crop Yields: A Bayesian Approach for Characterizing Technological Change," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277253, International Association of Agricultural Economists.
    14. Bollinger, Christopher R. & van Hasselt, Martijn, 2017. "Bayesian moment-based inference in a regression model with misclassification error," Journal of Econometrics, Elsevier, vol. 200(2), pages 282-294.
    15. Siddharta Chib & Minchul Shin & Anna Simoni, 2016. "Bayesian Empirical Likelihood Estimation and Comparison of Moment Condition Models," Working Papers 2016-21, Center for Research in Economics and Statistics.
    16. A Ford Ramsey, 2020. "Probability Distributions of Crop Yields: A Bayesian Spatial Quantile Regression Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(1), pages 220-239, January.
    17. Michael Kohler & Adam Krzyżak & Reinhard Tent & Harro Walk, 2018. "Nonparametric quantile estimation using importance sampling," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(2), pages 439-465, April.
    18. Yunwen Yang & Huixia Judy Wang & Xuming He, 2016. "Posterior Inference in Bayesian Quantile Regression with Asymmetric Laplace Likelihood," International Statistical Review, International Statistical Institute, vol. 84(3), pages 327-344, December.
    19. Korobilis, Dimitris, 2017. "Quantile regression forecasts of inflation under model uncertainty," International Journal of Forecasting, Elsevier, vol. 33(1), pages 11-20.
    20. 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.
    21. Yuanying Zhao & Dengke Xu, 2023. "A Bayesian Variable Selection Method for Spatial Autoregressive Quantile Models," Mathematics, MDPI, vol. 11(4), pages 1-19, February.

  14. Jun, Sung Jae & Pinkse, Joris & Wan, Yuanyuan, 2010. "A consistent nonparametric test of affiliation in auction models," Journal of Econometrics, Elsevier, vol. 159(1), pages 46-54, November.

    Cited by:

    1. Hanming Fang & Xun Tang, 2013. "Inference of Bidders' Risk Attitudes in Ascending Auctions with Endogenous Entry," NBER Working Papers 19435, National Bureau of Economic Research, Inc.
    2. Hanming Fang & Xun Tang, 2013. "Inference of Bidders’ Risk Attitudes in Ascending Auctions with Endogenous Entry," PIER Working Paper Archive 13-056, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    3. Yulia Kotlyarova & Marcia M Schafgans & Victoria Zinde-Walsh, 2011. "Adapting Kernel Estimation to Uncertain Smoothness," STICERD - Econometrics Paper Series 557, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    4. Luciano De Castro, 2010. "Affiliation, Equilibrium Existence and Revenue Ranking of Auctions," Discussion Papers 1530, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    5. Hickman Brent R. & Hubbard Timothy P. & Sağlam Yiğit, 2012. "Structural Econometric Methods in Auctions: A Guide to the Literature," Journal of Econometric Methods, De Gruyter, vol. 1(1), pages 67-106, August.
    6. Ma, Jun & Marmer, Vadim & Shneyerov, Artyom, 2019. "Inference for first-price auctions with Guerre, Perrigne, and Vuong’s estimator," Journal of Econometrics, Elsevier, vol. 211(2), pages 507-538.
    7. Aradillas-López, Andrés & Gandhi, Amit & Quint, Daniel, 2016. "A simple test for moment inequality models with an application to English auctions," Journal of Econometrics, Elsevier, vol. 194(1), pages 96-115.
    8. Hanming Fang & Ami Ko, 2018. "Partial Rating Area Offering in the ACA Marketplaces: Facts, Theory and Evidence," PIER Working Paper Archive 18-025, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Oct 2018.
    9. Tong Li & Jingfeng Lu & Li Zhao, 2015. "Auctions with selective entry and risk averse bidders: theory and evidence," RAND Journal of Economics, RAND Corporation, vol. 46(3), pages 524-545, September.
    10. Nianqing Liu & Yao Luo, 2014. "A Nonparametric Test of Exogenous Participation in First-Price Auctions," Working Papers tecipa-519, University of Toronto, Department of Economics.
    11. Sağlam, Yiğit, 2012. "Structural Econometric Methods in Auctions: A Guide to the Literature," Working Paper Series 19224, Victoria University of Wellington, The New Zealand Institute for the Study of Competition and Regulation.

  15. Jun, Sung Jae & Pinkse, Joris, 2009. "Semiparametric tests of conditional moment restrictions under weak or partial identification," Journal of Econometrics, Elsevier, vol. 152(1), pages 3-18, September.

    Cited by:

    1. El Ghouch, Anouar & Genton, Marc G. & Bouezmarni , Taoufik, 2012. "Measuring the Discrepancy of a Parametric Model via Local Polynomial Smoothing," LIDAM Discussion Papers ISBA 2012001, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Fan, Yanqin & Liu, Ruixuan, 2018. "Partial identification and inference in censored quantile regression," Journal of Econometrics, Elsevier, vol. 206(1), pages 1-38.
    3. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    4. Wang, Wenju & Wang, Qiao, 2019. "Consistent specification test for partially linear models with the k-nearest-neighbor method," Economics Letters, Elsevier, vol. 177(C), pages 89-93.
    5. Anouar El Ghouch & Marc G. Genton & Taoufik Bouezmarni, 2013. "Measuring the Discrepancy of a Parametric Model via Local Polynomial Smoothing," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(3), pages 455-470, September.
    6. Kohtaro Hitomi & Masamune Iwasawa & Yoshihiko Nishiyama, 2018. "Rate Optimal Specification Test When the Number of Instruments is Large," KIER Working Papers 986, Kyoto University, Institute of Economic Research.
    7. Antoine, Bertille & Lavergne, Pascal, 2014. "Conditional moment models under semi-strong identification," Journal of Econometrics, Elsevier, vol. 182(1), pages 59-69.
    8. Manuel Landajo & Celia Bilbao & Amelia Bilbao, 2012. "Nonparametric neural network modeling of hedonic prices in the housing market," Empirical Economics, Springer, vol. 42(3), pages 987-1009, June.
    9. Li, Hongjun & Li, Qi & Liu, Ruixuan, 2016. "Consistent model specification tests based on k-nearest-neighbor estimation method," Journal of Econometrics, Elsevier, vol. 194(1), pages 187-202.

  16. Jun, Sung Jae & Pinkse, Joris, 2009. "Adding Regressors To Obtain Efficiency," Econometric Theory, Cambridge University Press, vol. 25(1), pages 298-301, February.

    Cited by:

    1. Bo Jiang & Yongge Tian, 2022. "Equivalence Analysis of Statistical Inference Results under True and Misspecified Multivariate Linear Models," Mathematics, MDPI, vol. 11(1), pages 1-16, December.

  17. Jun, Sung Jae, 2009. "Local structural quantile effects in a model with a nonseparable control variable," Journal of Econometrics, Elsevier, vol. 151(1), pages 82-97, July.

    Cited by:

    1. Stefan Hoderlein & Yuya Sasaki, 2013. "Outcome Conditioned Treatment Effects," Boston College Working Papers in Economics 840, Boston College Department of Economics.
    2. Jun, Sung Jae & Pinkse, Joris & Xu, Haiqing, 2011. "Tighter bounds in triangular systems," Journal of Econometrics, Elsevier, vol. 161(2), pages 122-128, April.
    3. Victor Chernozhukov & Ivan Fernandez-Val & Whitney K. Newey & Sami Stouli & Francis Vella, 2017. "Semiparametric estimation of structural functions in nonseparable triangular models," CeMMAP working papers CWP48/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Fernandez-Val , Ivan & van Vuuren, Aico & Vella, Francis, 2018. "Nonseparable Sample Selection Models with Censored Selection Rules," Working Papers in Economics 716, University of Gothenburg, Department of Economics.
    5. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    6. Stefan Hoderlein & Hajo Holzmann & Alexander Meister, 2015. "The triangular model with random coefficients," CeMMAP working papers CWP33/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. 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.
    8. Ivan Fernandez-Val & Franco Peracchi & Francis Vella & Aico van Vuuren, 2019. "Decomposing Changes in the Distribution of Real Hourly Wages in the U.S," CeMMAP working papers CWP61/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Henderson, Daniel J. & Kumbhakar, Subal C. & Li, Qi & Parmeter, Christopher F., 2015. "Smooth coefficient estimation of a seemingly unrelated regression," Journal of Econometrics, Elsevier, vol. 189(1), pages 148-162.
    10. Victor Chernozhukov & Ivan Fernandez-Val & Amanda Kowalski, 2011. "Quantile Regression with Censoring and Endogeneity," Cowles Foundation Discussion Papers 1797, Cowles Foundation for Research in Economics, Yale University.
    11. Christina Christou & Ruthira Naraidoo & Rangan Gupta & Christis Hassapis, 2019. "Monetary Policy Reaction to Uncertainty in Japan: Evidence from a Quantile-on-Quantile Interest Rate Rule," Working Papers 201929, University of Pretoria, Department of Economics.
    12. Jiang, Yonghong & Wang, Jieru & Ao, Zhiming & Wang, Yujou, 2022. "The relationship between green bonds and conventional financial markets: Evidence from quantile-on-quantile and quantile coherence approaches," Economic Modelling, Elsevier, vol. 116(C).
    13. Wüthrich, Kaspar, 2019. "A closed-form estimator for quantile treatment effects with endogeneity," Journal of Econometrics, Elsevier, vol. 210(2), pages 219-235.
    14. Nese Yildiz, 2012. "Estimation of Binary Choice Models with Linear Index and Dummy Endogenous Variables," Koç University-TUSIAD Economic Research Forum Working Papers 1202, Koc University-TUSIAD Economic Research Forum.
    15. 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.
    16. Santiago Pereda Fernández, 2016. "Estimation of counterfactual distributions with a continuous endogenous treatment," Temi di discussione (Economic working papers) 1053, Bank of Italy, Economic Research and International Relations Area.

  18. Jun, Sung Jae & Pinkse, Joris, 2009. "Efficient Semiparametric Seemingly Unrelated Quantile Regression Estimation," Econometric Theory, Cambridge University Press, vol. 25(5), pages 1392-1414, October.

    Cited by:

    1. Habert white & Tae-Hwan Kim & Simone Manganelli, 2012. "VAR for VaR: Measuring Tail Dependence Using Multivariate Regression Quantiles," Working papers 2012rwp-45, Yonsei University, Yonsei Economics Research Institute.
    2. Henderson, Daniel J. & Kumbhakar, Subal C. & Li, Qi & Parmeter, Christopher F., 2015. "Smooth coefficient estimation of a seemingly unrelated regression," Journal of Econometrics, Elsevier, vol. 189(1), pages 148-162.
    3. Petrella, Lea & Raponi, Valentina, 2019. "Joint estimation of conditional quantiles in multivariate linear regression models with an application to financial distress," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 70-84.
    4. Lee, Dong Jin & Kim, Tae-Hwan & Mizen, Paul, 2021. "Impulse response analysis in conditional quantile models with an application to monetary policy," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    5. Jungmo Yoon & Antonio F. Galvao, 2020. "Cluster robust covariance matrix estimation in panel quantile regression with individual fixed effects," Quantitative Economics, Econometric Society, vol. 11(2), pages 579-608, May.
    6. Tae-Hwan Kim & Dong Jin Lee & Paul Mizen, 2020. "Impulse Response Analysis in Conditional Quantile Models and an Application to Monetary Policy," Working papers 2020rwp-164, Yonsei University, Yonsei Economics Research Institute.
    7. Lee, Dong Jin & Yoon, Jai Hyung, 2016. "The New Keynesian Phillips Curve in multiple quantiles and the asymmetry of monetary policy," Economic Modelling, Elsevier, vol. 55(C), pages 102-114.
    8. Li, Hongjun & Li, Qi & Liu, Ruixuan, 2016. "Consistent model specification tests based on k-nearest-neighbor estimation method," Journal of Econometrics, Elsevier, vol. 194(1), pages 187-202.

  19. Jun, Sung Jae, 2008. "Weak identification robust tests in an instrumental quantile model," Journal of Econometrics, Elsevier, vol. 144(1), pages 118-138, May.

    Cited by:

    1. Galvao, Antonio F. & Montes-Rojas, Gabriel, 2015. "On the equivalence of instrumental variables estimators for linear models," Economics Letters, Elsevier, vol. 134(C), pages 13-15.
    2. Isaiah Andrews & Anna Mikusheva, 2016. "Conditional Inference With a Functional Nuisance Parameter," Econometrica, Econometric Society, vol. 84, pages 1571-1612, July.
    3. Javier Alejo & Antonio F Galvao & Gabriel Montes-Rojas, 2023. "A first-stage representation for instrumental variables quantile regression," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 350-377.
    4. Kaplan, David M. & Sun, Yixiao, 2012. "Smoothed Estimating Equations For Instrumental Variables Quantile Regression," University of California at San Diego, Economics Working Paper Series qt888657tp, Department of Economics, UC San Diego.
    5. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    6. Joel L. Horowitz, 2017. "Non-asymptotic inference in instrumental variables estimation," CeMMAP working papers CWP46/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Victor Chernozhukov & Christian Hansen, 2013. "Quantile models with endogeneity," CeMMAP working papers CWP25/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Marmer, Vadim & Sakata, Shinichi, 2011. "Instrumental Variables Estimation and Weak-Identification-Robust Inference Based on a Conditional Quantile Restriction," Microeconomics.ca working papers vadim_marmer-2011-26, Vancouver School of Economics, revised 28 Sep 2011.
    9. Tae-Hwan Kim & Christophe Muller, 2013. "A Test for Endogeneity in Conditional Quantiles," AMSE Working Papers 1342, Aix-Marseille School of Economics, France, revised Aug 2013.
    10. Victor Chernozhukov & Christian Hansen & Kaspar Wuthrich, 2020. "Instrumental Variable Quantile Regression," Papers 2009.00436, arXiv.org.
    11. Jun, Sung Jae, 2009. "Local structural quantile effects in a model with a nonseparable control variable," Journal of Econometrics, Elsevier, vol. 151(1), pages 82-97, July.
    12. 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.
    13. Tae-Hwan Kim & Christophe Muller, 2015. "A Particular Form of Non-Constant Effect in Two-Stage Quantile Regression," Working papers 2015rwp-82, Yonsei University, Yonsei Economics Research Institute.
    14. Claude Diebolt & Tapas Mishra & Faustine Perrin, 2021. "Gender empowerment as an enforcer of individuals’ choice between education and fertility : Evidence from 19th century France," Post-Print hal-03345562, HAL.
    15. 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.
    16. Philip Kostov & Julie Le Gallo, 2018. "What role for human capital in the growth process: new evidence from endogenous latent factor panel quantile regressions," Scottish Journal of Political Economy, Scottish Economic Society, vol. 65(5), pages 501-527, November.
    17. 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.
    18. Joel L. Horowitz, 2017. "Non-asymptotic inference in instrumental variables estimation," CeMMAP working papers 46/17, Institute for Fiscal Studies.
    19. Joel L. Horowitz, 2018. "Non-Asymptotic Inference in Instrumental Variables Estimation," Papers 1809.03600, arXiv.org.
    20. Horowitz, Joel L., 2021. "Bounding the difference between true and nominal rejection probabilities in tests of hypotheses about instrumental variables models," Journal of Econometrics, Elsevier, vol. 222(2), pages 1057-1082.
    21. Joel L. Horowitz, 2018. "Non-asymptotic inference in instrumental variables estimation," CeMMAP working papers CWP52/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    22. Isaiah Andrews & Anna Mikusheva, 2016. "Conditional Inference With a Functional Nuisance Parameter," Econometrica, Econometric Society, vol. 84(4), pages 1571-1612, July.

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NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 7 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) 2006-04-22 2014-09-25 2018-12-24 2020-05-04 2022-07-11. Author is listed
  2. NEP-BIG: Big Data (3) 2020-05-04 2021-07-19 2022-07-11
  3. NEP-CMP: Computational Economics (1) 2022-07-11
  4. NEP-DCM: Discrete Choice Models (1) 2021-07-26

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