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sivqr: Smoothed IV quantile regression

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In this article, I introduce the sivqr command, which estimates the coefficients of the instrumental variables (IV) quantile regression model introduced by Chernozhukov and Hansen (2005). This model complements the alternative models underlying the commands cqiv, ivqreg2, and ivqte, and the sivqr command offers advantages over the apocryphal ivqreg command. Computationally, sivqr implements the smoothed estimator of Kaplan and Sun (2017), who show the smoothing improves both computation time and statistical accuracy. Standard errors are computed by Bayesian bootstrap; for non-i.i.d. sampling, sivqr is compatible with the bootstrap and svy bootstrap prefixes. I discuss syntax and the underlying methodology. Simulation and empirical examples illustrate the new sivqr command

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  • David M. Kaplan, 2020. "sivqr: Smoothed IV quantile regression," Working Papers 2009, Department of Economics, University of Missouri.
    • Handle: RePEc:umc:wpaper:2009
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    1. Victor Chernozhukov & Ivan Fernández-Val & Sukjin Han & Amanda Kowalski, 2019. "Censored quantile instrumental-variable estimation with Stata," Stata Journal, StataCorp LP, vol. 19(4), pages 768-781, December.
    2. Chernozhukov, Victor & Fernández-Val, Iván & Kowalski, Amanda E., 2015. "Quantile regression with censoring and endogeneity," Journal of Econometrics, Elsevier, vol. 186(1), pages 201-221.
    3. Machado, José A.F. & Santos Silva, J.M.C., 2019. "Quantiles via moments," Journal of Econometrics, Elsevier, vol. 213(1), pages 145-173.
    4. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    5. Chamberlain, Gary & Imbens, Guido W, 2003. "Nonparametric Applications of Bayesian Inference," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 12-18, January.
    6. 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.
    7. Markus Frolich & Blaise Melly, 2010. "Estimation of quantile treatment effects with Stata," Stata Journal, StataCorp LP, vol. 10(3), pages 423-457, September.
    8. 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.
    9. Lee, Sokbae, 2007. "Endogeneity in quantile regression models: A control function approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 1131-1158, December.
    10. Guido W. Imbens & Whitney K. Newey, 2009. "Identification and Estimation of Triangular Simultaneous Equations Models Without Additivity," Econometrica, Econometric Society, vol. 77(5), pages 1481-1512, September.
    11. Kathryn Graddy, 1995. "Testing for Imperfect Competition at the Fulton Fish Market," RAND Journal of Economics, The RAND Corporation, vol. 26(1), pages 75-92, Spring.
    12. Victor Chernozhukov & Christian Hansen, 2005. "An IV Model of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 73(1), pages 245-261, January.
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    Cited by:

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    2. Vardges Hovhannisyan & Vahé Heboyan & Magdana Kondaridze, 2024. "An empirical assessment of effectiveness of the US tobacco control policies: a smoothed instrumental variables quantile regression approach," Empirical Economics, Springer, vol. 67(2), pages 465-493, August.
    3. Feito-Ruiz, Isabel & Menéndez-Requejo, Susana, 2022. "Debt maturity in family firms: Heterogeneity across countries," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    4. Alfonso Rosolia, 2021. "Does information about current inflation affect expectations and decisions? Another look at Italian firms," Temi di discussione (Economic working papers) 1353, Bank of Italy, Economic Research and International Relations Area.

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    Keywords

    endogeneity; instrumental variables; structural;
    All these keywords.

    JEL classification:

    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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