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Quantile and average effects in nonseparable panel models

Author

Listed:
  • Victor Chernozhukov

    (Institute for Fiscal Studies and MIT)

  • Ivan Fernandez-Val

    (Institute for Fiscal Studies and Boston University)

  • Whitney K. Newey

    (Institute for Fiscal Studies and MIT)

Abstract
This paper gives identification and estimation results for quantile and average effects in nonseparable panel models, when the distribution of period specific disturbances does not vary over time. Bounds are given for interesting effects with discrete regressors that are strictly exogenous or predetermined. We allow for location and scale time effects and show how monotonicity can be used to shrink the bounds. We derive rates at which the bounds tighten as the number T of time series observations grows and give an empirical illustration.

Suggested Citation

  • Victor Chernozhukov & Ivan Fernandez-Val & Whitney K. Newey, 2009. "Quantile and average effects in nonseparable panel models," CeMMAP working papers CWP29/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:29/09
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    File URL: http://cemmap.ifs.org.uk/wps/cwp2909.pdf
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    Cited by:

    1. Kato, Kengo & F. Galvao, Antonio & Montes-Rojas, Gabriel V., 2012. "Asymptotics for panel quantile regression models with individual effects," Journal of Econometrics, Elsevier, vol. 170(1), pages 76-91.
    2. Fernando Ubeda & Francisco Pérez-Hernández, 2017. "Absorptive Capacity and Geographical Distance Two Mediating Factors of FDI Spillovers: a Threshold Regression Analysis for Spanish Firms," Journal of Industry, Competition and Trade, Springer, vol. 17(1), pages 1-28, March.
    3. Rosen, Adam M., 2012. "Set identification via quantile restrictions in short panels," Journal of Econometrics, Elsevier, vol. 166(1), pages 127-137.
    4. Stefan Hoderlein & Yuya Sasaki, 2011. "On the role of time in nonseparable panel data models," CeMMAP working papers CWP15/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. David Powell, 2010. "Unconditional Quantile Treatment Effects in the Presence of Covariates," Working Papers WR-816, RAND Corporation.
    6. David Powell, 2010. "Unconditional Quantile Treatment Effects in the Presence of Covariates," Working Papers 816, RAND Corporation.
    7. Amaresh Tiwari & Franz Palm, 2011. "Nonlinear Panel Data Models with Expected a Posteriori Values of Correlated Random Effects," CREPP Working Papers 1113, Centre de Recherche en Economie Publique et de la Population (CREPP) (Research Center on Public and Population Economics) HEC-Management School, University of Liège.
    8. Gao, Yichen & Li, Cong & Liang, Zhongwen, 2015. "Binary response correlated random coefficient panel data models," Journal of Econometrics, Elsevier, vol. 188(2), pages 421-434.
    9. Charles F. Manski & John V. Pepper, 2011. "Deterrence and the Death Penalty: Partial Identification Analysis Using Repeated Cross Sections," NBER Working Papers 17455, National Bureau of Economic Research, Inc.
    10. repec:ran:wpaper:710-1 is not listed on IDEAS
    11. Harding, Matthew & Lamarche, Carlos, 2014. "Estimating and testing a quantile regression model with interactive effects," Journal of Econometrics, Elsevier, vol. 178(P1), pages 101-113.

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