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Censored Quantile Instrumental Variable Estimation via Control Functions

Author

Listed:
  • Victor Chernozhukov

    (Department of Economics, MIT)

  • Ivan Fernandez-Val

    (Boston University, Department of Economics)

  • Amanda E. Kowalski

    (Department of Economics, Yale University)

Abstract

In this paper, we develop a new censored quantile instrumental variable (CQIV) estimator and describe its properties and computation. The CQIV estimator handles censoring semi-parametrically in the tradition of Powell (1986), and it generalizes standard censored quantile regression (CQR) methods to incorporate endogenous regressors in a manner that is computationally tractable. Our computational algorithm combines a control function approach with the CQR estimator developed by Chernozhukov and Hong (2002). Through Monte-Carlo simulation, we show that CQIV performs well relative to Tobit IV in terms of bias and dispersion in a model that satisfies the parametric assumptions required for Tobit IV to be efficient. Given the strong parametric assumptions required by Tobit IV, the gains to CQIV relative to Tobit IV are likely to be large in empirical applications. We present results from an empirical application of CQIV to the estimation of Engel curves for alcohol. This empirical application demonstrates the importance of accounting for censoring and endogeneity with CQIV.

Suggested Citation

  • Victor Chernozhukov & Ivan Fernandez-Val & Amanda E. Kowalski, "undated". "Censored Quantile Instrumental Variable Estimation via Control Functions," Boston University - Department of Economics - Working Papers Series wp2009-012, Boston University - Department of Economics.
  • Handle: RePEc:bos:wpaper:wp2009-012
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    Cited by:

    1. Ubfal, Diego, 2016. "How general are time preferences? Eliciting good-specific discount rates," Journal of Development Economics, Elsevier, vol. 118(C), pages 150-170.
    2. Kwak, Sungil, 2011. "The Impact of Taxes on Charitable Giving: Empirical Evidence from the Korean Labor and Income Panel Study," MPRA Paper 36845, University Library of Munich, Germany.
    3. Pravin K. Trivedi, 2010. "Keynote lecture: Estimation of count-data panel models," Mexican Stata Users' Group Meetings 2010 06, Stata Users Group.

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