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Tikhonov Regularization for Nonparametric Instrumental Variable Estimators

Collection
  • Cahiers de recherche; 2006.08
Publication date2006
Abstract

We study a Tikhonov Regularized (TiR) estimator of a functional parameter identified by conditional moment restrictions in a linear model with both exogenous and endogenous regressorts. The nonparametric instrumental variable estimator is based on a minimum distance principle with penalization by the norms of the parameter and its derivative. After showing its consisteny in the Sobolev norm we derive the expression of the asymptotic Mean Integrated Square Error. The convergence rate with optimal value of the regularization parameter is characterized in two examples. We illustrate our theoretical findings and the small sample properties with simulation results. Finally, we provide an empirical appli8cation to estimation of an Engel curve, and discuss a data driven selection procedure for the regularization parameter.

Keywords
  • Minimum Distance
  • Nonparametric Estimation
  • Ill-posed Inverse Problems
  • Tikhonov Regularization
  • Endogeneity
  • Instrumental Variable
  • Engel curve
Citation (ISO format)
GAGLIARDINI, P., SCAILLET, Olivier. Tikhonov Regularization for Nonparametric Instrumental Variable Estimators. 2006
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  • PID : unige:5742
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Creation04/15/2010 12:19:40 PM
First validation04/15/2010 12:19:40 PM
Update time03/14/2023 3:26:24 PM
Status update03/14/2023 3:26:24 PM
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