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Chuan Goh

Personal Details

First Name:Chuan
Middle Name:
Last Name:Goh
Suffix:
RePEc Short-ID:pgo112
https://www.ocf.berkeley.edu/~scgoh/
Department of Economics and Finance University of Guelph 50 Stone Road East Guelph, ON N1G 2W1 Canada

Affiliation

Department of Economics and Finance
Gordon Lang School of Business and Economics
University of Guelph

Guelph, Canada
http://www.uoguelph.ca/economics/
RePEc:edi:degueca (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Juan Carlos Escanciano & Chuan Goh, 2018. "Quantile-Regression Inference With Adaptive Control of Size," Papers 1807.06977, arXiv.org, revised Sep 2019.
  2. Chuan Goh, 2017. "Rate-Optimal Estimation of the Intercept in a Semiparametric Sample-Selection Model," Papers 1710.01423, arXiv.org, revised Sep 2018.
  3. Juan Carlos Escanciano & Chuan Goh, 2010. "Specification Analysis of Structural Quantile Regression Models," Working Papers tecipa-415, University of Toronto, Department of Economics.
  4. Chuan Goh, 2009. "Efficient Semiparametric Detection of Changes in Trend," Working Papers tecipa-373, University of Toronto, Department of Economics.
  5. Chuan Goh, 2009. "Nonstandard Estimation of Inverse Conditional Density-Weighted Expectations," Working Papers tecipa-374, University of Toronto, Department of Economics.
  6. Chuan Goh, 2009. "Bootstrap-based Bandwidth Selection for Semiparametric Generalized Regression Estimators," Working Papers tecipa-375, University of Toronto, Department of Economics.
  7. Chuan Goh, 2007. "Bandwidth Selection for Semiparametric Estimators Using the m-out-of-n Bootstrap," Working Papers tecipa-274, University of Toronto, Department of Economics.
  8. Chuan Goh, 2007. "Nonparametric Inferences on Conditional Quantile Processes," Working Papers tecipa-277, University of Toronto, Department of Economics.

Articles

  1. J. C. Escanciano & S. C. Goh, 2019. "Quantile-Regression Inference With Adaptive Control of Size," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(527), pages 1382-1393, July.
  2. Escanciano, J.C. & Goh, S.C., 2014. "Specification analysis of linear quantile models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 495-507.
  3. S. Goh, 2012. "Design-adaptive nonparametric estimation of conditional quantile derivatives," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(3), pages 597-612.
  4. Goh, S.C. & Knight, K., 2009. "Nonstandard Quantile-Regression Inference," Econometric Theory, Cambridge University Press, vol. 25(5), pages 1415-1432, October.
  5. S. C. Goh, 2005. "Simple Edgeworth approximations for semiparametric averaged derivatives," Economics Bulletin, AccessEcon, vol. 3(50), pages 1-8.

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. Juan Carlos Escanciano & Chuan Goh, 2018. "Quantile-Regression Inference With Adaptive Control of Size," Papers 1807.06977, arXiv.org, revised Sep 2019.

    Cited by:

    1. Gimenes, Nathalie & Guerre, Emmanuel, 2022. "Quantile regression methods for first-price auctions," Journal of Econometrics, Elsevier, vol. 226(2), pages 224-247.

  2. Chuan Goh, 2017. "Rate-Optimal Estimation of the Intercept in a Semiparametric Sample-Selection Model," Papers 1710.01423, arXiv.org, revised Sep 2018.

    Cited by:

    1. Kanaya, Shin & Taylor, Luke, 2020. "Type I and Type II Error Probabilities in the Courtroom," MPRA Paper 100217, University Library of Munich, Germany.
    2. Arulampalam, Wiji & Corradi, Valentina & Gutknecht, Daniel, 2021. "Intercept Estimation in Nonlinear Selection Models," IZA Discussion Papers 14364, Institute of Labor Economics (IZA).

  3. Juan Carlos Escanciano & Chuan Goh, 2010. "Specification Analysis of Structural Quantile Regression Models," Working Papers tecipa-415, University of Toronto, Department of Economics.

    Cited by:

    1. Christoph Rothe & Dominik Wied, 2013. "Misspecification Testing in a Class of Conditional Distributional Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 314-324, March.

Articles

  1. J. C. Escanciano & S. C. Goh, 2019. "Quantile-Regression Inference With Adaptive Control of Size," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(527), pages 1382-1393, July.
    See citations under working paper version above.
  2. Escanciano, J.C. & Goh, S.C., 2014. "Specification analysis of linear quantile models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 495-507.

    Cited by:

    1. Julio Galvez & Javier Mencía, 2014. "Distributional Linkages between European Sovereign Bond and Bank Asset Returns," Working Papers wp2014_1407, CEMFI.
    2. Jorge E. Galán, 2020. "The benefits are at the tail: uncovering the impact of macroprudential policy on growth-at-risk," Working Papers 2007, Banco de España.
    3. Russell Davidson & Victoria Zinde‐Walsh, 2017. "Advances in specification testing," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 50(5), pages 1595-1631, December.
    4. Conde-Amboage, Mercedes & Sánchez-Sellero, César & González-Manteiga, Wenceslao, 2015. "A lack-of-fit test for quantile regression models with high-dimensional covariates," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 128-138.
    5. Jorge E. Galán & María Rodríguez Moreno, 2020. "At-risk measures and financial stability," Revista de Estabilidad Financiera, Banco de España, issue Autumn.
    6. Gijbels, Irène & Omelka, Marek & Veraverbeke, Noël, 2021. "Omnibus test for covariate effects in conditional copula models," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    7. Pedro H. C. Sant'Anna & Xiaojun Song, 2016. "Specification Tests for the Propensity Score," Papers 1611.06217, arXiv.org, revised Feb 2019.
    8. 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.
    9. Ana Pérez-González & Tomás R. Cotos-Yáñez & Wenceslao González-Manteiga & Rosa M. Crujeiras-Casais, 2021. "Goodness-of-fit tests for quantile regression with missing responses," Statistical Papers, Springer, vol. 62(3), pages 1231-1264, June.
    10. Christoph Breunig, 2016. "Specification Testing in Nonparametric Instrumental Quantile Regression," SFB 649 Discussion Papers SFB649DP2016-032, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    11. Gimenes, Nathalie & Guerre, Emmanuel, 2022. "Quantile regression methods for first-price auctions," Journal of Econometrics, Elsevier, vol. 226(2), pages 224-247.
    12. Pedro H. C. Sant'Anna & Xiaojun Song, 2020. "Specification tests for generalized propensity scores using double projections," Papers 2003.13803, arXiv.org.
    13. Christoph Breunig, 2019. "Specification Testing in Nonparametric Instrumental Quantile Regression," Papers 1909.10129, arXiv.org.

  3. Goh, S.C. & Knight, K., 2009. "Nonstandard Quantile-Regression Inference," Econometric Theory, Cambridge University Press, vol. 25(5), pages 1415-1432, October.

    Cited by:

    1. David M. Kaplan, 2013. "Improved Quantile Inference Via Fixed-Smoothing Asymptotics And Edgeworth Expansion," Working Papers 1313, Department of Economics, University of Missouri.
    2. Marcelo Fernandes & Emmanuel Guerre & Eduardo Horta, 2019. "Smoothing quantile regressions," Papers 1905.08535, arXiv.org, revised Aug 2019.
    3. Fan, Yanqin & Liu, Ruixuan, 2016. "A direct approach to inference in nonparametric and semiparametric quantile models," Journal of Econometrics, Elsevier, vol. 191(1), pages 196-216.
    4. Christis Katsouris, 2022. "Asymptotic Theory for Moderate Deviations from the Unit Boundary in Quantile Autoregressive Time Series," Papers 2204.02073, arXiv.org.

More information

Research fields, statistics, top rankings, if available.

Statistics

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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 8 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 (8) 2007-01-13 2007-01-23 2009-10-10 2009-10-10 2009-10-24 2010-11-27 2017-10-08 2018-08-20. Author is listed
  2. NEP-ETS: Econometric Time Series (2) 2007-01-13 2009-10-10

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