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Instrumental variables quantile regression for panel data with measurement errors

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  • Galvao Jr, A. F.
  • Montes-Rojas, G.

Abstract

This paper develops an instrumental variables estimator for quantile regression in panel data with fixed effects. Asymptotic properties of the instrumental variables estimator are studied for large N and T when Na/T ! 0, for some a > 0. Wald and Kolmogorov-Smirnov type tests for general linear restrictions are developed. The estimator is applied to the problem of measurement errors in variables, which induces endogeneity and as a result bias in the model. We derive an approximation to the bias in the quantile regression fixed effects estimator in the presence of measurement error and show its connection to similar effects in standard least squares models. Monte Carlo simulations are conducted to evaluate the finite sample properties of the estimator in terms of bias and root mean squared error. Finally, the methods are applied to a model of firm investment. The results show interesting heterogeneity in the Tobin’s q and cash flow sensitivities of investment. In both cases, the sensitivities are monotonically increasing along the quantiles.

Suggested Citation

  • Galvao Jr, A. F. & Montes-Rojas, G., 2009. "Instrumental variables quantile regression for panel data with measurement errors," Working Papers 09/06, Department of Economics, City University London.
    • Handle: RePEc:cty:dpaper:09/06
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    File URL: https://openaccess.city.ac.uk/id/eprint/1496/1/Instrumental_Variables_Quantile_Regression.pdf
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    Cited by:

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    3. Alessia Matano & Paolo Naticchioni, 2009. "Wage distribution and the spatial sorting of workers and firms," Working Papers - Dipartimento di Economia 8-DEISFOL, Dipartimento di Economia, Sapienza University of Rome, revised 2009.
    4. Ozan Bakis & Nurhan Davutyan & Haluk Levent & Sezgin Polat, 2013. "Quantile Estimates for Social Returns to Education in Turkey: 2006–2009," Middle East Development Journal, Taylor & Francis Journals, vol. 5(3), pages 1350017-131, January.
    5. Le Wang, 2013. "How Does Education Affect the Earnings Distribution in Urban China?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(3), pages 435-454, June.
    6. Matano, Alessia & Naticchioni, Paolo, 2013. "Rent sharing as a driver of the glass ceiling effect," Economics Letters, Elsevier, vol. 118(1), pages 55-59.
    7. Andini, Corrado & Andini, Monica, 2015. "A Note on Unemployment Persistence and Quantile Parameter Heterogeneity," IZA Discussion Papers 8819, Institute of Labor Economics (IZA).
    8. Daniel Wilhelm, 2015. "Identification and estimation of nonparametric panel data regressions with measurement error," CeMMAP working papers CWP34/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Li Tao & Lingnan Tai & Manling Qian & Maozai Tian, 2023. "A New Instrumental-Type Estimator for Quantile Regression Models," Mathematics, MDPI, vol. 11(15), pages 1-26, August.

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