IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2301.07241.html
   My bibliography  Save this paper

Unconditional Quantile Partial Effects via Conditional Quantile Regression

Author

Listed:
  • Javier Alejo
  • Antonio F. Galvao
  • Julian Martinez-Iriarte
  • Gabriel Montes-Rojas

Abstract

This paper develops a semi-parametric procedure for estimation of unconditional quantile partial effects using quantile regression coefficients. The estimator is based on an identification result showing that, for continuous covariates, unconditional quantile effects are a weighted average of conditional ones at particular quantile levels that depend on the covariates. We propose a two-step estimator for the unconditional effects where in the first step one estimates a structural quantile regression model, and in the second step a nonparametric regression is applied to the first step coefficients. We establish the asymptotic properties of the estimator, say consistency and asymptotic normality. Monte Carlo simulations show numerical evidence that the estimator has very good finite sample performance and is robust to the selection of bandwidth and kernel. To illustrate the proposed method, we study the canonical application of the Engel's curve, i.e. food expenditures as a share of income.

Suggested Citation

  • Javier Alejo & Antonio F. Galvao & Julian Martinez-Iriarte & Gabriel Montes-Rojas, 2023. "Unconditional Quantile Partial Effects via Conditional Quantile Regression," Papers 2301.07241, arXiv.org, revised Dec 2023.
    • Handle: RePEc:arx:papers:2301.07241
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2301.07241
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. Fortin, Nicole & Lemieux, Thomas & Firpo, Sergio, 2011. "Decomposition Methods in Economics," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 1, pages 1-102, Elsevier.
    3. Bera Anil K. & Galvao Antonio F. & Montes-Rojas Gabriel V. & Park Sung Y., 2016. "Asymmetric Laplace Regression: Maximum Likelihood, Maximum Entropy and Quantile Regression," Journal of Econometric Methods, De Gruyter, vol. 5(1), pages 79-101, January.
    4. Andrews, Donald W.K., 1995. "Nonparametric Kernel Estimation for Semiparametric Models," Econometric Theory, Cambridge University Press, vol. 11(3), pages 560-586, June.
    5. Song, Kyungchul, 2008. "Uniform Convergence Of Series Estimators Over Function Spaces," Econometric Theory, Cambridge University Press, vol. 24(6), pages 1463-1499, December.
    6. José Mata & José A. F. Machado, 2005. "Counterfactual decomposition of changes in wage distributions using quantile regression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 445-465.
    7. Julian Martinez-Iriarte & YiXiao Sun, 2022. "Identification and Estimation of Unconditional Policy Effects of an Endogenous Binary Treatment: an Unconditional MTE Approach," Working Papers 131, Red Nacional de Investigadores en Economía (RedNIE).
    8. Christoph Rothe, 2012. "Partial Distributional Policy Effects," Econometrica, Econometric Society, vol. 80(5), pages 2269-2301, September.
    9. Atsushi Inoue & Tong Li & Qi Xu, 2021. "Two Sample Unconditional Quantile Effect," Papers 2105.09445, arXiv.org.
    10. Andreas Chai & Alessio Moneta, 2010. "Retrospectives: Engel Curves," Journal of Economic Perspectives, American Economic Association, vol. 24(1), pages 225-240, Winter.
    11. Joshua Angrist & Victor Chernozhukov & Iván Fernández-Val, 2006. "Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure," Econometrica, Econometric Society, vol. 74(2), pages 539-563, March.
    12. Stefan Sperlich, 2009. "A note on non-parametric estimation with predicted variables," Econometrics Journal, Royal Economic Society, vol. 12(2), pages 382-395, July.
    13. Peter Hall & Joel L. Horowitz, 2013. "A simple bootstrap method for constructing nonparametric confidence bands for functions," CeMMAP working papers CWP29/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643, June.
    15. Peter Hall & Joel L. Horowitz, 2013. "A simple bootstrap method for constructing nonparametric confidence bands for functions," CeMMAP working papers 29/13, Institute for Fiscal Studies.
    16. Antonio F. Galvao & Liang Wang, 2015. "Uniformly Semiparametric Efficient Estimation of Treatment Effects With a Continuous Treatment," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1528-1542, December.
    17. Julián Martínez-Iriarte, 2021. "Sensitivity analysis in unconditional quantile effects," Working Papers 52, Red Nacional de Investigadores en Economía (RedNIE).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Martínez-Iriarte, Julián & Montes-Rojas, Gabriel & Sun, Yixiao, 2024. "Unconditional effects of general policy interventions," Journal of Econometrics, Elsevier, vol. 238(2).

    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.
    1. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.
    2. Martínez-Iriarte, Julián & Montes-Rojas, Gabriel & Sun, Yixiao, 2024. "Unconditional effects of general policy interventions," Journal of Econometrics, Elsevier, vol. 238(2).
    3. Ghosh, Pallab Kumar, 2014. "The contribution of human capital variables to changes in the wage distribution function," Labour Economics, Elsevier, vol. 28(C), pages 58-69.
    4. Manuel Arellano & Stéphane Bonhomme, 2017. "Quantile Selection Models With an Application to Understanding Changes in Wage Inequality," Econometrica, Econometric Society, vol. 85, pages 1-28, January.
    5. Männasoo, Kadri, 2022. "Working hours and gender wage differentials: Evidence from the American Working Conditions Survey," Labour Economics, Elsevier, vol. 76(C).
    6. Mammen, Enno & Rothe, Christoph & Schienle, Melanie, 2016. "Semiparametric Estimation With Generated Covariates," Econometric Theory, Cambridge University Press, vol. 32(5), pages 1140-1177, October.
    7. Leonid Kogan & Dimitris Papanikolaou & Lawrence D. W. Schmidt & Jae Song, 2020. "Technological Innovation and Labor Income Risk," NBER Working Papers 26964, National Bureau of Economic Research, Inc.
    8. Sonja C. Kassenboehmer & Mathias G. Sinning, 2014. "Distributional Changes in the Gender Wage Gap," ILR Review, Cornell University, ILR School, vol. 67(2), pages 335-361, April.
    9. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2022. "Unconditional quantile regression with high‐dimensional data," Quantitative Economics, Econometric Society, vol. 13(3), pages 955-978, July.
    10. Pallab Ghosh & Jae Lee, 2016. "Decomposition of Changes in Korean Wage Inequality, 1998–2007," Journal of Labor Research, Springer, vol. 37(1), pages 1-28, March.
    11. Ying-Ying Lee, 2018. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Papers 1811.00157, arXiv.org.
    12. Samantha Leorato & Franco Peracchi, 2015. "Shape Regressions," EIEF Working Papers Series 1506, Einaudi Institute for Economics and Finance (EIEF), revised Jul 2015.
    13. Iv'an Fern'andez-Val & Franco Peracchi & Aico van Vuuren & Francis Vella, 2018. "Selection and the Distribution of Female Hourly Wages in the U.S," Papers 1901.00419, arXiv.org, revised Jan 2022.
    14. VAN KERM Philippe & YU Seunghee & CHOE Chung, 2014. "Wage differentials between native, immigrant and cross-border workers: Evidence and model comparisons," LISER Working Paper Series 2014-05, Luxembourg Institute of Socio-Economic Research (LISER).
    15. Victor Chernozhukov & Iván Fernández-Val & Blaise Melly, 2022. "Fast algorithms for the quantile regression process," Empirical Economics, Springer, vol. 62(1), pages 7-33, January.
    16. Sakellariou, Chris, 2012. "Decomposing the increase in TIMSS Scores in Ghana : 2003-2007," Policy Research Working Paper Series 6084, The World Bank.
    17. Sergio P. Firpo & Nicole M. Fortin & Thomas Lemieux, 2018. "Decomposing Wage Distributions Using Recentered Influence Function Regressions," Econometrics, MDPI, vol. 6(2), pages 1-40, May.
    18. Juan Carlos Escanciano & Lin Zhu, 2013. "Set inferences and sensitivity analysis in semiparametric conditionally identified models," CeMMAP working papers CWP55/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    19. 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.
    20. Thomschke, Lorenz, 2015. "Changes in the distribution of rental prices in Berlin," Regional Science and Urban Economics, Elsevier, vol. 51(C), pages 88-100.

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    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:arx:papers:2301.07241. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.