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Sung Jae Jun

Personal Details

First Name:Sung Jae
Middle Name:
Last Name:Jun
Suffix:
RePEc Short-ID:pju58
http://www.econ.psu.edu/~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 & 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 & Xu, Haiqing, 2011. "Tighter bounds in triangular systems," Journal of Econometrics, Elsevier, vol. 161(2), pages 122-128, April.
  2. 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.
  3. Tony Lancaster & Sung Jae Jun, 2010. "Bayesian quantile regression methods," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 287-307.
  4. 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.
  5. 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.
  6. 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.
  7. Jun, Sung Jae & Pinkse, Joris, 2009. "Adding Regressors To Obtain Efficiency," Econometric Theory, Cambridge University Press, vol. 25(1), pages 298-301, February.
  8. 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.
  9. 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.

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 & 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, Open Access Journal, vol. 6(3), pages 1-47, September.

Articles

  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.

    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. 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.
    3. Christoph Rothe, 2012. "Partial Distributional Policy Effects," Econometrica, Econometric Society, vol. 80(5), pages 2269-2301, September.
    4. 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.
    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. Sungwon Lee, 2020. "Identifucation and Confidence Regions for Treatment Effect and its Distribution under Stochastic Dominance," Working Papers 2011, Research Institute for Market Economy, Sogang University.
    7. Jun Ma & Vadim Marmer & Zhengfei Yu, 2021. "Inference on Individual Treatment Effects in Nonseparable Triangular Models," Papers 2107.05559, arXiv.org, revised Jul 2021.
    8. Desire Kedagni & Ismael Mourifie, 2014. "Tightening Bounds In Triangular Systems," Working Papers tecipa-515, University of Toronto, Department of Economics.
    9. 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.
    10. Andrew Chesher & Adam Rosen, 2012. "Simultaneous equations for discrete outcomes: coherence, completeness, and identification," CeMMAP working papers CWP21/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Kasy, Maximilian, "undated". "Instrumental variables with unrestricted heterogeneity and continuous treatment - DON'T CITE! SEE ERRATUM BELOW," Working Paper 33257, Harvard University OpenScholar.
    12. Lee, Jinhyun, 2013. "Sharp Bounds on Heterogeneous Individual Treatment Responses," SIRE Discussion Papers 2013-89, Scottish Institute for Research in Economics (SIRE).
    13. Maximilian Kasy, 2014. "Instrumental Variables with Unrestricted Heterogeneity and Continuous Treatment," Review of Economic Studies, Oxford University Press, vol. 81(4), pages 1614-1636.
    14. Jinhyun Lee, 2013. "Sharp Bounds on Heterogeneous Individual Treatment Responses," Discussion Paper Series, School of Economics and Finance 201310, School of Economics and Finance, University of St Andrews.
    15. 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.
    16. 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.
    17. Sung Jae Jun & Joris Pinkse & Haiqing Xu & Neşe Yıldız, 2016. "Multiple Discrete Endogenous Variables in Weakly-Separable Triangular Models," Econometrics, MDPI, Open Access Journal, vol. 4(1), pages 1-21, February.

  2. 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. 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.
    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," Working Paper series 15_14, Rimini Centre for Economic Analysis.
    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. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Korobilis, Dimitris, 2015. "Quantile forecasts of inflation under model uncertainty," SIRE Discussion Papers 2015-72, Scottish Institute for Research in Economics (SIRE).
    14. Korobilis, Dimitris, 2017. "Quantile regression forecasts of inflation under model uncertainty," International Journal of Forecasting, Elsevier, vol. 33(1), pages 11-20.
    15. Philip Kostov, 2013. "Empirical likelihood estimation of the spatial quantile regression," Journal of Geographical Systems, Springer, vol. 15(1), pages 51-69, January.
    16. 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.
    17. Dries Benoit & Rahim Alhamzawi & Keming Yu, 2013. "Bayesian lasso binary quantile regression," Computational Statistics, Springer, vol. 28(6), pages 2861-2873, December.

  3. 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 Schafgans & Victoria Zinde-Walsh, 2011. "Adapting Kernel Estimation to Uncertain Smoothness," Working Papers daleconwp2011-01, Dalhousie University, Department of Economics.
    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. Hanming Fang & Ami Ko, 2018. "Partial Rating Area Offering in the ACA Marketplaces: Facts, Theory and Evidence," NBER Working Papers 25154, National Bureau of Economic Research, Inc.
    8. 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.
    9. 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.

  4. 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. 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.
    3. Fan, Yanqin & Liu, Ruixuan, 2018. "Partial identification and inference in censored quantile regression," Journal of Econometrics, Elsevier, vol. 206(1), pages 1-38.
    4. 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.
    5. Antoine, Bertille & Lavergne, Pascal, 2014. "Conditional moment models under semi-strong identification," Journal of Econometrics, Elsevier, vol. 182(1), pages 59-69.
    6. 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.
    7. 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.

  5. 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. 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.
    3. 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.
    4. 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.
    5. Dong Jin Lee & Tae-Hwan Kim & Paul Mizen, 2020. "Impulse response analysis in conditional quantile models with an application to monetary policy," Discussion Papers 2020/08, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    6. 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.
    7. 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.
    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.

  6. 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. Victor Chernozhukov & Ivan Fernandez-Val & Amanda Kowalski, 2011. "Quantile regression with censoring and endogeneity," CeMMAP working papers CWP20/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Wüthrich, Kaspar, 2019. "A closed-form estimator for quantile treatment effects with endogeneity," Journal of Econometrics, Elsevier, vol. 210(2), pages 219-235.
    3. Jun, Sung Jae & Pinkse, Joris & Xu, Haiqing, 2011. "Tighter bounds in triangular systems," Journal of Econometrics, Elsevier, vol. 161(2), pages 122-128, April.
    4. 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.
    5. 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.
    6. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.

  7. 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. 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.
    4. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    5. Victor Chernozhukov & Christian Hansen & Kaspar Wuthrich, 2020. "Instrumental Variable Quantile Regression," Papers 2009.00436, arXiv.org.
    6. 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.
    7. Tae-Hwan Kim & Christophe Muller, 2015. "A Particular Form of Non-Constant Effect in Two-Stage Quantile Regression," AMSE Working Papers 1522, Aix-Marseille School of Economics, France, revised May 2015.
    8. Isaiah Andrews & Anna Mikusheva, 2016. "Conditional Inference With a Functional Nuisance Parameter," Econometrica, Econometric Society, vol. 84(4), pages 1571-1612, July.
    9. Diebolt, Claude & Mishra, Tapas & Perrin, Faustine, 2021. "Gender empowerment as an enforcer of individuals’ choice between education and fertility: Evidence from 19th century France," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 408-438.
    10. 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.
    11. Javier Alejo & Antonio F. Galvao & Gabriel Montes-Rojas, 2021. "A first-stage representation for instrumental variables quantile regression," Papers 2102.01212, arXiv.org, revised Sep 2021.

Software components

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More information

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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 1 paper 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 (1) 2006-04-22

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