Smoothed Estimating Equations For Instrumental Variables Quantile Regression
AbstractThe moment conditions or estimating equations for instrumental variables quantile regression involves the discontinuous indicator function. We instead use smoothed estimating equations, with bandwidth h. This is known to allow higher-order expansions that justify bootstrap refinements for inference. Computation of the estimator also becomes simpler and more reliable, especially with (more) endogenous regressors. We show that the mean squared error of the vector of estimating equations is minimized for some h > 0, which also reduces the mean squared error of the parameter estimators. The same h also minimizes higher-order type I error for a Ãâ¡2 test, leading to improved size-adjusted power. Our plug-in bandwidth consistently reproduces all of these properties in simulations.
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Bibliographic InfoPaper provided by Department of Economics, UC San Diego in its series University of California at San Diego, Economics Working Paper Series with number qt888657tp.
Date of creation: 01 Jan 2012
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Social and Behavioral Sciences; Physical Sciences and Mathematics; Edgeworth expansion; Instrumental variable; Optimal smoothing parameter choice; Quantile regression; Smoothed estimating equation.;
Other versions of this item:
- David Kaplan & Yixiao Sun, 2013. "Smoothed Estimating Equations for Instrumental Variables Quantile Regression," Working Papers 1314, Department of Economics, University of Missouri.
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- 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
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- Peter C.B. Phillips & Joon Y. Park, 1986.
"On the Formulation of Wald Tests of Nonlinear Restrictions,"
Cowles Foundation Discussion Papers
801, Cowles Foundation for Research in Economics, Yale University.
- Phillips, Peter C B & Park, Joon Y, 1988. "On the Formulation of Wald Tests of Nonlinear Restrictions," Econometrica, Econometric Society, vol. 56(5), pages 1065-83, September.
- Yoon-Jae Whang, 2004.
"Smoothed Empirical Likelihood Methods for Quantile Regression Models,"
Cowles Foundation Discussion Papers
1453, Cowles Foundation for Research in Economics, Yale University.
- Whang, Yoon-Jae, 2006. "Smoothed Empirical Likelihood Methods For Quantile Regression Models," Econometric Theory, Cambridge University Press, vol. 22(02), pages 173-205, April.
- Yoon-Jae Whang, 2003. "Smoothed Empirical Likelihood Methods for Quantile Regression Models," Econometrics 0310005, EconWPA.
- Joel L. Horowitz, 1996.
"Bootstrap Methods for Median Regression Models,"
- Whitney K. Newey, 2004. "Efficient Semiparametric Estimation via Moment Restrictions," Econometrica, Econometric Society, vol. 72(6), pages 1877-1897, November.
- Cattaneo, Matias D. & Crump, Richard K. & Jansson, Michael, 2012.
"Optimal inference for instrumental variables regression with non-Gaussian errors,"
Journal of Econometrics,
Elsevier, vol. 167(1), pages 1-15.
- Mathias D. Cattaneo & Richard K. Crump & Michael Jansson, 2007. "Optimal Inference for Instrumental Variables Regression with non-Gaussian Errors," CREATES Research Papers 2007-11, School of Economics and Management, University of Aarhus.
- Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
- Chernozhukov, Victor & Hansen, Christian & Jansson, Michael, 2009. "Finite sample inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 152(2), pages 93-103, October.
- Horowitz, Joel L., 2002. "Bootstrap critical values for tests based on the smoothed maximum score estimator," Journal of Econometrics, Elsevier, vol. 111(2), pages 141-167, December.
- Otsu, Taisuke, 2008. "Conditional empirical likelihood estimation and inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 142(1), pages 508-538, January.
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