k-Class Instrumental Variables Quantile Regression
Author
Abstract
Suggested Citation
Download full text from publisher
Other versions of this item:
- David M. Kaplan & Xin Liu, 2024. "k-Class instrumental variables quantile regression," Empirical Economics, Springer, vol. 67(1), pages 111-141, July.
References listed on IDEAS
- Motohiro Yogo, 2004. "Estimating the Elasticity of Intertemporal Substitution When Instruments Are Weak," The Review of Economics and Statistics, MIT Press, vol. 86(3), pages 797-810, August.
- Nelson, Charles R & Startz, Richard, 1990.
"Some Further Results on the Exact Small Sample Properties of the Instrumental Variable Estimator,"
Econometrica, Econometric Society, vol. 58(4), pages 967-976, July.
- Nelson, C. & Startz, R., 1988. "Some Furthere Results On The Exact Small Sample Properties Of The Instrumental Variable Estimator," Discussion Papers in Economics at the University of Washington 88-06, Department of Economics at the University of Washington.
- Charles R. Nelson & Richard Startz, 1988. "Some Further Results on the Exact Small Sample Properties of the Instrumental Variable Estimator," NBER Technical Working Papers 0068, National Bureau of Economic Research, Inc.
- Nelson, C. & Startz, R., 1988. "Some Furthere Results On The Exact Small Sample Properties Of The Instrumental Variable Estimator," Working Papers 88-06, University of Washington, Department of Economics.
- Blomquist, Soren & Dahlberg, Matz, 1999. "Small Sample Properties of LIML and Jackknife IV Estimators: Experiments with Weak Instruments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(1), pages 69-88, Jan.-Feb..
- Luciano de Castro & Antonio F. Galvao, 2019. "Dynamic Quantile Models of Rational Behavior," Econometrica, Econometric Society, vol. 87(6), pages 1893-1939, November.
- Fuller, Wayne A, 1977. "Some Properties of a Modification of the Limited Information Estimator," Econometrica, Econometric Society, vol. 45(4), pages 939-953, May.
- 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.
- 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.
- Luciano de Castro & Antonio F. Galvao & David M. Kaplan & Xin Liu, 2018. "Smoothed GMM for quantile models," Working Papers 1803, Department of Economics, University of Missouri.
- Douglas Staiger & James H. Stock, 1997.
"Instrumental Variables Regression with Weak Instruments,"
Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
- Douglas Staiger & James H. Stock, 1994. "Instrumental Variables Regression with Weak Instruments," NBER Technical Working Papers 0151, National Bureau of Economic Research, Inc.
- Angrist, J D & Imbens, G W & Krueger, A B, 1999.
"Jackknife Instrumental Variables Estimation,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(1), pages 57-67, Jan.-Feb..
- Joshua D. Angrist & Guido W. Imbens & Alan Krueger, 1995. "Jackknife Instrumental Variables Estimation," NBER Technical Working Papers 0172, National Bureau of Economic Research, Inc.
- Stock, James H & Wright, Jonathan H & Yogo, Motohiro, 2002. "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 518-529, October.
- John C. Chao & Norman R. Swanson, 2005.
"Consistent Estimation with a Large Number of Weak Instruments,"
Econometrica, Econometric Society, vol. 73(5), pages 1673-1692, September.
- Chao, John Chao & Norman R. Swanson, 2003. "Consistent Estimation with a Large Number of Weak Instruments," Cowles Foundation Discussion Papers 1417, Cowles Foundation for Research in Economics, Yale University.
- John C. Chao & Norman Rasmus Swanson, 2004. "Consistent Estimation with a Large Number of Weak Instruments," Yale School of Management Working Papers ysm374, Yale School of Management.
- John Chao & Norman Swanson, 2004. "Consistent Estimation with a Large Number of Weak Instruments," Departmental Working Papers 200421, Rutgers University, Department of Economics.
- Kaplan, David M. & Sun, Yixiao, 2017.
"Smoothed Estimating Equations For Instrumental Variables Quantile Regression,"
Econometric Theory, Cambridge University Press, vol. 33(1), pages 105-157, February.
- 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.
- David M. Kaplan & Yixiao Sun, 2013. "Smoothed Estimating Equations for Instrumental Variables Quantile Regression," Working Papers 1314, Department of Economics, University of Missouri.
- David M. Kaplan & Yixiao Sun, 2016. "Smoothed estimating equations for instrumental variables quantile regression," Papers 1609.09033, arXiv.org.
- 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.
- Javier Alejo & Antonio F. Galvao & Gabriel Montes-Rojas, 2021. "A first-stage representation for instrumental variables quantile regression," Papers 2102.01212, arXiv.org, revised Feb 2022.
- 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.
- 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.
- Luciano de Castro & Antonio F. Galvao & David M. Kaplan & Xin Liu, 2018. "Smoothed GMM for quantile models," Working Papers 1803, Department of Economics, University of Missouri.
- Victor Chernozhukov & Christian Hansen, 2005. "An IV Model of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 73(1), pages 245-261, January.
- Chernozhukov, Victor & Hansen, Christian, 2006. "Instrumental quantile regression inference for structural and treatment effect models," Journal of Econometrics, Elsevier, vol. 132(2), pages 491-525, June.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- 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.
- Guilhem Bascle, 2008. "Controlling for endogeneity with instrumental variables in strategic management research," Post-Print hal-00576795, HAL.
- de Castro, Luciano & Galvao, Antonio F. & Montes-Rojas, Gabriel, 2020. "Quantile selection in non-linear GMM quantile models," Economics Letters, Elsevier, vol. 195(C).
- Carlos Velasco & Xuexin Wang, 2021. "Instrumental variable estimation via a continuum of instruments with an application to estimating the elasticity of intertemporal substitution in consumption," Working Papers 2024-09-06, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
- Namhyun Kim & Winfried Pohlmeier, 2016. "A Note on the Regularized Approach to Biased 2SLS Estimation with Weak Instruments," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(6), pages 915-924, December.
- Russell Davidson & James G. MacKinnon, 2006.
"The case against JIVE,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 827-833, September.
- James G. MacKinnon & Russell Davidson, 2006. "The case against JIVE," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 827-833.
- James G. MacKinnon & Russell Davidson, 2004. "The Case Against Jive," Working Paper 1031, Economics Department, Queen's University.
- Russell Davidson & James MacKinnon, 2006. "The Case Against Jive," Departmental Working Papers 2004-02, McGill University, Department of Economics.
- Keisuke Hirano & Jack R. Porter, 2015. "Location Properties of Point Estimators in Linear Instrumental Variables and Related Models," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 720-733, December.
- Markus Frölich & Michael Lechner, 2004.
"Regional treatment intensity as an instrument for the evaluation of labour market policies,"
University of St. Gallen Department of Economics working paper series 2004
2004-08, Department of Economics, University of St. Gallen.
- Frölich, Markus & Lechner, Michael, 2004. "Regional Treatment Intensity as an Instrument for the Evaluation of Labour Market Policies," IZA Discussion Papers 1095, Institute of Labor Economics (IZA).
- Lechner, Michael & Fröhlich, Markus, 2004. "Regional Treatment Intensity as an Instrument for the Evaluation of Labour Market Policies," CEPR Discussion Papers 4304, C.E.P.R. Discussion Papers.
- 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.
- Javier Alejo & Antonio F. Galvao & Gabriel Montes-Rojas, 2021. "A first-stage representation for instrumental variables quantile regression," Papers 2102.01212, arXiv.org, revised Feb 2022.
- Nam-Hyun Kim & Winfried Pohlmeier, 2015. "A Regularization Approach to Biased Two-Stage Least Squares Estimation," Working Paper series 15-22, Rimini Centre for Economic Analysis.
- Xin Liu, 2024.
"Averaging Estimation for Instrumental Variables Quantile Regression,"
Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(5), pages 1290-1312, October.
- Xin Liu, 2019. "Averaging estimation for instrumental variables quantile regression," Working Papers 1907, Department of Economics, University of Missouri.
- Xin Liu, 2019. "Averaging estimation for instrumental variables quantile regression," Papers 1910.04245, arXiv.org.
- Hiroaki Kaido & Kaspar Wüthrich, 2021.
"Decentralization estimators for instrumental variable quantile regression models,"
Quantitative Economics, Econometric Society, vol. 12(2), pages 443-475, May.
- Hiroaki Kaido & Kaspar Wuthrich, 2018. "Decentralization Estimators for Instrumental Variable Quantile Regression Models," Papers 1812.10925, arXiv.org, revised Sep 2020.
- 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.
- Kaido, Hiroaki & Wüthrich, Kaspar, 2021. "Decentralization estimators for instrumental variable quantile regression models," University of California at San Diego, Economics Working Paper Series qt362921wv, Department of Economics, UC San Diego.
- Hiroaki Kaido & Kaspar Wüthrich, 2019. "Decentralization estimators for instrumental variable quantile regression models," CeMMAP working papers CWP42/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- 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.
- 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.
- Luciano de Castro & Antonio F. Galvao & David M. Kaplan & Xin Liu, 2018. "Smoothed GMM for quantile models," Working Papers 1803, Department of Economics, University of Missouri.
- John C. Chao & Norman R. Swanson, 2005.
"Consistent Estimation with a Large Number of Weak Instruments,"
Econometrica, Econometric Society, vol. 73(5), pages 1673-1692, September.
- Chao, John Chao & Norman R. Swanson, 2003. "Consistent Estimation with a Large Number of Weak Instruments," Cowles Foundation Discussion Papers 1417, Cowles Foundation for Research in Economics, Yale University.
- John Chao & Norman Swanson, 2004. "Consistent Estimation with a Large Number of Weak Instruments," Departmental Working Papers 200421, Rutgers University, Department of Economics.
- John C. Chao & Norman Rasmus Swanson, 2004. "Consistent Estimation with a Large Number of Weak Instruments," Yale School of Management Working Papers ysm374, Yale School of Management.
- Murray Michael P., 2017. "Linear Model IV Estimation When Instruments Are Many or Weak," Journal of Econometric Methods, De Gruyter, vol. 6(1), pages 1-22, January.
- Emma M. Iglesias & Garry D. A. Phillips, 2012.
"Almost Unbiased Estimation in Simultaneous Equation Models With Strong and/or Weak Instruments,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 505-520, June.
- Iglesias, Emma M. & Phillips, Garry D.A., 2011. "Almost Unbiased Estimation in Simultaneous Equations Models with Strong and / or Weak Instruments," Cardiff Economics Working Papers E2011/19, Cardiff University, Cardiff Business School, Economics Section.
- He, Xuming & Pan, Xiaoou & Tan, Kean Ming & Zhou, Wen-Xin, 2023. "Smoothed quantile regression with large-scale inference," Journal of Econometrics, Elsevier, vol. 232(2), pages 367-388.
- Hansen, Christian & Kozbur, Damian, 2014. "Instrumental variables estimation with many weak instruments using regularized JIVE," Journal of Econometrics, Elsevier, vol. 182(2), pages 290-308.
- Chao, John C. & Swanson, Norman R. & Woutersen, Tiemen, 2023. "Jackknife estimation of a cluster-sample IV regression model with many weak instruments," Journal of Econometrics, Elsevier, vol. 235(2), pages 1747-1769.
- 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.
- 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.
- Luciano de Castro & Antonio F. Galvao & David M. Kaplan & Xin Liu, 2018. "Smoothed GMM for quantile models," Working Papers 1803, Department of Economics, University of Missouri.
More about this item
Keywords
bias; weak instruments;JEL classification:
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2021-06-14 (Econometrics)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:umc:wpaper:2104. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chao Gu (email available below). General contact details of provider: https://edirc.repec.org/data/edumous.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.