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Inference for Optimal Split Point in Conditional Quantiles

Author

Listed:
  • fany88@uw.edu

    (Department of Economics, University of Washington, Seattle, WA 98195, USA)

  • Ruixuan Liu

    (Department of Economics, Emory University, Atlanta, GA 30322, USA)

  • Dongming Zhu

    (School of Economics & Key Laboratory of Mathematical Economics, Shanghai University of Finance and Economics, Shanghai 200433, China)

Abstract

In this paper we show the occurrence of cubic-root asymptotics in misspecified conditional quantile models where the approximating functions are restricted to be binary decision trees. Inference procedure for the optimal split point in the decision tree is conducted by inverting a t-test or a deviation measure test, both involving Chernoff type limiting distributions. In order to avoid estimating the nuisance parameters in the complicated limiting distribution, subsampling is proved to deliver the correct confidence interval/set.

Suggested Citation

  • fany88@uw.edu & Ruixuan Liu & Dongming Zhu, 2016. "Inference for Optimal Split Point in Conditional Quantiles," Frontiers of Economics in China-Selected Publications from Chinese Universities, Higher Education Press, vol. 11(1), pages 40-59, March.
    • Handle: RePEc:fec:journl:v:11:y:2016:i:1:p:40-59
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    File URL: http://journal.hep.com.cn/fec/EN/10.3868/s060-005-016-0004-6
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    More about this item

    Keywords

    cubic-root asymptotics; Chernof distribution; misspecified Quantile regression; optimal split point;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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