IDEAS home Printed from https://ideas.repec.org/f/pso554.html
   My authors  Follow this author

Suyong Song

Personal Details

First Name:Suyong
Middle Name:
Last Name:Song
Suffix:
RePEc Short-ID:pso554
[This author has chosen not to make the email address public]
https://sites.google.com/site/suyongsong/
Terminal Degree:2010 Department of Economics; University of California-San Diego (UCSD) (from RePEc Genealogy)

Affiliation

Department of Economics
Tippie College of Business
University of Iowa

Iowa City, Iowa (United States)
http://tippie.uiowa.edu/economics/
RePEc:edi:deuiaus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Suyong Song & Stephen S. Baek, 2019. "Shape Matters: Evidence from Machine Learning on Body Shape-Income Relationship," Papers 1906.06747, arXiv.org.
  2. Ardakani, Omid & Kishor, Kundan & Song, Suyong, 2015. "On the Effectiveness of Inflation Targeting: Evidence from a Semiparametric Approach," MPRA Paper 75091, University Library of Munich, Germany.

Articles

  1. Kyoo il Kim & Suyong Song, 2022. "Control variables approach to estimate semiparametric models of mismeasured endogenous regressors with an application to U.K. twin data," Econometric Reviews, Taylor & Francis Journals, vol. 41(4), pages 448-483, April.
  2. Liqiong Chen & Antonio F. Galvao & Suyong Song, 2021. "Quantile Regression with Generated Regressors," Econometrics, MDPI, vol. 9(2), pages 1-35, April.
  3. Suyong Song & Stephen Baek, 2021. "Body shape matters: Evidence from machine learning on body shape-income relationship," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-17, July.
  4. Ardakani, Omid M. & Kishor, N. Kundan & Song, Suyong, 2018. "Re-evaluating the effectiveness of inflation targeting," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 76-97.
  5. Antonio F. Galvao & Gabriel Montes–Rojas & Jose Olmo & Suyong Song, 2018. "On solving endogeneity with invalid instruments: an application to investment equations," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 689-716, June.
  6. Galvao, Antonio F. & Montes-Rojas, Gabriel & Song, Suyong, 2017. "Endogeneity bias modeling using observables," Economics Letters, Elsevier, vol. 152(C), pages 41-45.
  7. Firpo, Sergio & Galvao, Antonio F. & Song, Suyong, 2017. "Measurement errors in quantile regression models," Journal of Econometrics, Elsevier, vol. 198(1), pages 146-164.
  8. Kim, Kyoo il & Petrin, Amil & Song, Suyong, 2016. "Estimating production functions with control functions when capital is measured with error," Journal of Econometrics, Elsevier, vol. 190(2), pages 267-279.
  9. Song, Suyong, 2015. "Semiparametric estimation of models with conditional moment restrictions in the presence of nonclassical measurement errors," Journal of Econometrics, Elsevier, vol. 185(1), pages 95-109.
  10. Kishor, N. Kundan & Kumari, Swati & Song, Suyong, 2015. "Time variation in the relative importance of permanent and transitory components in the U.S. housing market," Finance Research Letters, Elsevier, vol. 12(C), pages 92-99.
  11. Suyong Song & Susanne M. Schennach & Halbert White, 2015. "Estimating nonseparable models with mismeasured endogenous variables," Quantitative Economics, Econometric Society, vol. 6(3), pages 749-794, November.

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

    Sorry, no citations of working papers recorded.

Articles

  1. Liqiong Chen & Antonio F. Galvao & Suyong Song, 2021. "Quantile Regression with Generated Regressors," Econometrics, MDPI, vol. 9(2), pages 1-35, April.

    Cited by:

    1. Hartwig, Benny & Meinerding, Christoph & Schüler, Yves S., 2021. "Identifying indicators of systemic risk," Journal of International Economics, Elsevier, vol. 132(C).
    2. Jayeeta Bhattacharya, 2020. "Quantile regression with generated dependent variable and covariates," Papers 2012.13614, arXiv.org.
    3. Dianliang Deng & Mashfiqul Huq Chowdhury, 2022. "Quantile Regression Approach for Analyzing Similarity of Gene Expressions under Multiple Biological Conditions," Stats, MDPI, vol. 5(3), pages 1-23, July.

  2. Ardakani, Omid M. & Kishor, N. Kundan & Song, Suyong, 2018. "Re-evaluating the effectiveness of inflation targeting," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 76-97.

    Cited by:

    1. Ahmad Zubaidi Baharumshah & Siew-Voon Soon & Mark E. Wohar, 2021. "Phillips Curve for the Asian Economies: A Nonlinear Perspective," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(12), pages 3508-3537, September.
    2. Geoffrey R. Dunbar & Amy (Qijia) Li, 2019. "The Effects of Inflation Targeting for Financial Development," Staff Analytical Notes 2019-21, Bank of Canada.
    3. Bruno Ferreira Frascaroli & Wellington Charles Lacerda Nobrega, 2019. "Inflation Targeting and Inflation Risk in Latin America," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(11), pages 2389-2408, September.
    4. López-Villavicencio, Antonia & Pourroy, Marc, 2019. "Does inflation targeting always matter for the ERPT? A robust approach," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 360-377.
    5. Suh, Sangwon & Kim, Daehwan, 2021. "Inflation targeting and expectation anchoring: Evidence from developed and emerging market economies," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    6. Adina Ionela Străchinaru & Bogdan Andrei Dumitrescu, 2019. "Assessing the Sustainability of Inflation Targeting: Evidence from EU Countries with Non-EURO Currencies," Sustainability, MDPI, vol. 11(20), pages 1-13, October.
    7. Victor Pontines, 2020. "The real effects of loan-to-value limits: Empirical evidence from Korea," CAMA Working Papers 2020-02, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    8. Antonakakis, Nikolaos & Christou, Christina & Gil-Alana, Luis A. & Gupta, Rangan, 2021. "Inflation-targeting and inflation volatility: International evidence from the cosine-squared cepstrum," International Economics, Elsevier, vol. 167(C), pages 29-38.
    9. Farvaque, Etienne & Malan, Franck & Stanek, Piotr, 2020. "Misplaced childhood: When recession children grow up as central bankers," Journal of Economic Dynamics and Control, Elsevier, vol. 110(C).
    10. Martin Stojanovikj & Goran Petrevski, 2021. "Macroeconomic effects of inflation targeting in emerging market economies," Empirical Economics, Springer, vol. 61(5), pages 2539-2585, November.

  3. Antonio F. Galvao & Gabriel Montes–Rojas & Jose Olmo & Suyong Song, 2018. "On solving endogeneity with invalid instruments: an application to investment equations," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 689-716, June.

    Cited by:

    1. Ali Al-Sharadqah & Majid Mojirsheibani & William Pouliot, 2020. "On the performance of weighted bootstrapped kernel deconvolution density estimators," Statistical Papers, Springer, vol. 61(4), pages 1773-1798, August.

  4. Galvao, Antonio F. & Montes-Rojas, Gabriel & Song, Suyong, 2017. "Endogeneity bias modeling using observables," Economics Letters, Elsevier, vol. 152(C), pages 41-45.

    Cited by:

    1. Hoedoafia, Mabel Akosua, 2020. "On the Link between Trade Liberalization and Firm Productivity: Panel Data Evidence from Private Firms in Ghana," MPRA Paper 99568, University Library of Munich, Germany.
    2. Antonio F. Galvao & Gabriel Montes–Rojas & Jose Olmo & Suyong Song, 2018. "On solving endogeneity with invalid instruments: an application to investment equations," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 689-716, June.

  5. Firpo, Sergio & Galvao, Antonio F. & Song, Suyong, 2017. "Measurement errors in quantile regression models," Journal of Econometrics, Elsevier, vol. 198(1), pages 146-164.

    Cited by:

    1. Battistin, Erich & Lamarche, Carlos & Rettore, Enrico, 2020. "Quantiles of the Gain Distribution of an Early Childhood Intervention," IZA Discussion Papers 13101, Institute of Labor Economics (IZA).
    2. Battistin, Erich & Lamarche, Carlos & Rettore, Enrico, 2020. "Quantiles of the Gain Distribution of an Early Child Intervention," CEPR Discussion Papers 14721, C.E.P.R. Discussion Papers.
    3. Andrew Chesher, 2017. "Understanding the effect of measurement error on quantile regressions," CeMMAP working papers CWP19/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Zongwu Cai & Ying Fang & Ming Lin & Shengfang Tang, 2021. "A Nonparametric Test for Testing Heterogeneity in Conditional Quantile Treatment Effects," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202117, University of Kansas, Department of Economics, revised Aug 2021.
    5. Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Robust inference in deconvolution," Quantitative Economics, Econometric Society, vol. 12(1), pages 109-142, January.
    6. Zongwu Cai & Ying Fang & Ming Lin & Shengfang Tang, 2020. "Inferences for Partially Conditional Quantile Treatment Effect Model," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202005, University of Kansas, Department of Economics, revised Feb 2020.
    7. Liqiong Chen & Antonio F. Galvao & Suyong Song, 2021. "Quantile Regression with Generated Regressors," Econometrics, MDPI, vol. 9(2), pages 1-35, April.
    8. Brantly Callaway & Tong Li & Irina Murtazashvili, 2021. "Nonlinear Approaches to Intergenerational Income Mobility allowing for Measurement Error," Papers 2107.09235, arXiv.org, revised Dec 2021.

  6. Kim, Kyoo il & Petrin, Amil & Song, Suyong, 2016. "Estimating production functions with control functions when capital is measured with error," Journal of Econometrics, Elsevier, vol. 190(2), pages 267-279.

    Cited by:

    1. Sahoo, Pradipta Kumar & Rath, Badri Narayan & Le, Viet, 2022. "Nexus between export, productivity, and competitiveness in the Indian manufacturing sector," Journal of Asian Economics, Elsevier, vol. 79(C).
    2. Miao, Chenglin & Fang, Debin & Sun, Liyan & Luo, Qiaoling, 2017. "Natural resources utilization efficiency under the influence of green technological innovation," Resources, Conservation & Recycling, Elsevier, vol. 126(C), pages 153-161.
    3. Tsionas, Mike G. & Mallick, Sushanta K., 2019. "A Bayesian semiparametric approach to stochastic frontiers and productivity," European Journal of Operational Research, Elsevier, vol. 274(1), pages 391-402.
    4. Junrong Liu & Robin C. Sickles & E. G. Tsionas, 2017. "Bayesian Treatments for Panel Data Stochastic Frontier Models with Time Varying Heterogeneity," Econometrics, MDPI, vol. 5(3), pages 1-21, July.
    5. Hu, Yingyao & Huang, Guofang & Sasaki, Yuya, 2020. "Estimating production functions with robustness against errors in the proxy variables," Journal of Econometrics, Elsevier, vol. 215(2), pages 375-398.
    6. Collard-Wexler, Allan & De Loecker, Jan, 2016. "Production Function Estimation with Measurement Error in Inputs," CEPR Discussion Papers 11399, C.E.P.R. Discussion Papers.
    7. Allan Collard-Wexler & Jan De Loecker, 2016. "Production Function Estimation and Capital Measurement Error," NBER Working Papers 22437, National Bureau of Economic Research, Inc.
    8. Daniel A. Ackerberg & Kevin Caves & Garth Frazer, 2015. "Identification Properties of Recent Production Function Estimators," Econometrica, Econometric Society, vol. 83, pages 2411-2451, November.
    9. Yismaw Ayelign & Lakhwinder Singh, 2019. "Comparison of Recent Developments in Productivity Estimation: Application on Ethiopian Manufacturing Sector," Academic Journal of Economic Studies, Faculty of Finance, Banking and Accountancy Bucharest,"Dimitrie Cantemir" Christian University Bucharest, vol. 5(3), pages 20-31, September.
    10. Fu, Shihe & Xu, Xiaocong & Zhang, Junfu, 2021. "Land conversion across cities in China," Regional Science and Urban Economics, Elsevier, vol. 87(C).
    11. Abito, Jose Miguel, 2019. "Estimating Production Functions with Fixed Effects," MPRA Paper 97825, University Library of Munich, Germany.
    12. Daniel Gurara & Dawit Tessema, 2018. "Losing to Blackouts: Evidence from Firm Level Data," IMF Working Papers 2018/159, International Monetary Fund.

  7. Song, Suyong, 2015. "Semiparametric estimation of models with conditional moment restrictions in the presence of nonclassical measurement errors," Journal of Econometrics, Elsevier, vol. 185(1), pages 95-109.

    Cited by:

    1. Francis J. DiTraglia & Camilo Garcia-Jimeno, 2020. "Identifying the effect of a mis-classified, binary, endogenous regressor," Papers 2011.07272, arXiv.org.
    2. DiTraglia, Francis J. & García-Jimeno, Camilo, 2019. "Identifying the effect of a mis-classified, binary, endogenous regressor," Journal of Econometrics, Elsevier, vol. 209(2), pages 376-390.
    3. Denni Tommasi & Arthur Lewbel & Rossella Calvi, 2017. "LATE with Mismeasured or Misspecified Treatment: An application to Women's Empowerment in India," Working Papers ECARES ECARES 2017-27, ULB -- Universite Libre de Bruxelles.
    4. Suyong Song & Stephen S. Baek, 2019. "Shape Matters: Evidence from Machine Learning on Body Shape-Income Relationship," Papers 1906.06747, arXiv.org.
    5. Francis J. DiTraglia & Camilo García-Jimeno, 2017. "Mis-classified, Binary, Endogenous Regressors: Identification and Inference," NBER Working Papers 23814, National Bureau of Economic Research, Inc.
    6. Andrews, Donald W.K., 2017. "Examples of L2-complete and boundedly-complete distributions," Journal of Econometrics, Elsevier, vol. 199(2), pages 213-220.
    7. Kim, Kyoo il & Petrin, Amil & Song, Suyong, 2016. "Estimating production functions with control functions when capital is measured with error," Journal of Econometrics, Elsevier, vol. 190(2), pages 267-279.
    8. Francis DiTraglia & Camilo Garcia-Jimeno, 2015. "On Mis-measured Binary Regressors: New Results And Some Comments on the Literature, Second Version," PIER Working Paper Archive 15-039, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 11 Nov 2015.
    9. Francis DiTraglia & Camilo Garcia-Jimeno, 2015. "On Mis-measured Binary Regressors: New Results And Some Comments on the Literature, Third Version," PIER Working Paper Archive 15-040, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 24 Nov 2015.

  8. Suyong Song & Susanne M. Schennach & Halbert White, 2015. "Estimating nonseparable models with mismeasured endogenous variables," Quantitative Economics, Econometric Society, vol. 6(3), pages 749-794, November.

    Cited by:

    1. Denni Tommasi & Arthur Lewbel & Rossella Calvi, 2017. "LATE with Mismeasured or Misspecified Treatment: An application to Women's Empowerment in India," Working Papers ECARES ECARES 2017-27, ULB -- Universite Libre de Bruxelles.
    2. Takahide Yanagi, 2019. "Inference on local average treatment effects for misclassified treatment," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 938-960, September.
    3. Francis J. DiTraglia & Camilo García-Jimeno, 2017. "Mis-classified, Binary, Endogenous Regressors: Identification and Inference," NBER Working Papers 23814, National Bureau of Economic Research, Inc.
    4. Song, Suyong, 2015. "Semiparametric estimation of models with conditional moment restrictions in the presence of nonclassical measurement errors," Journal of Econometrics, Elsevier, vol. 185(1), pages 95-109.
    5. Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Robust inference in deconvolution," Quantitative Economics, Econometric Society, vol. 12(1), pages 109-142, January.
    6. Adusumilli, Karun & Otsu, Taisuke, 2018. "Nonparametric instrumental regression with errors in variables," LSE Research Online Documents on Economics 85871, London School of Economics and Political Science, LSE Library.
    7. Daniel Wilhelm, 2019. "Testing for the presence of measurement error," CeMMAP working papers CWP48/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Shiu, Ji-Liang, 2016. "Identification and estimation of endogenous selection models in the presence of misclassification errors," Economic Modelling, Elsevier, vol. 52(PB), pages 507-518.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 2 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-AGR: Agricultural Economics (1) 2019-06-24. Author is listed
  2. NEP-BIG: Big Data (1) 2019-06-24. Author is listed
  3. NEP-CBA: Central Banking (1) 2016-11-27. Author is listed
  4. NEP-MON: Monetary Economics (1) 2016-11-27. Author is listed
  5. NEP-PAY: Payment Systems & Financial Technology (1) 2019-06-24. Author is listed

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Suyong Song should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.