Arbeitspapier
Nonparametric instrumental variables estimation of a quantile regression model
We consider nonparametric estimation of a regression function that is identified by requiring a specified quantile of the regression error conditional on an instrumental variable to be zero. The resulting estimating equation is a nonlinear integral equation of the first kind, which generates an ill-posed-inverse problem. The integral operator and distribution of the instrumental variable are unknown and must be estimated nonparametrically. We show that the estimator is mean-square consistent, derive its rate of convergence in probability, and give conditions under which this rate is optimal in a minimax sense. The results of Monte Carlo experiments show that the estimator behaves well in finite samples.
- Sprache
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Englisch
- Erschienen in
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Series: cemmap working paper ; No. CWP09/06
Estimation: General
Multiple or Simultaneous Equation Models: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
Lee, Sokbae
- DOI
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doi:10.1920/wp.cem.2006.0906
- Handle
- Letzte Aktualisierung
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20.09.2024, 08:22 MESZ
Objekttyp
- Arbeitspapier
Beteiligte
- Horowitz, Joel
- Lee, Sokbae
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2006