Tikhonov Regularization for Functional Minimum Distance Estimators
Patrick Gagliardini () and
Olivier Scaillet ()
No 06-30, Swiss Finance Institute Research Paper Series from Swiss Finance Institute
We study the asymptotic properties of a Tikhonov Regularized (TiR) estimator of a functional parameter based on a minimum distance principle for nonparametric conditional moment restrictions. The estimator is computationally tractable and takes a closed form in the linear case. We derive its asymptotic Mean Integrated Squared Error (MISE), its rate of convergence and its pointwise asymptotic normality under a regularization parameter depending on sample size. The optimal value of the regularization parameter is characterized. We illustrate our theoretical findings and the small sample properties with simulation results for two numerical examples. We also discuss two data driven selection procedures of the regularization parameter via a spectral representation and a subsampling approximation of the MISE. Finally, we provide an empirical application to nonparametric estimation of an Engel curve.
Keywords: MinimumDistance; Nonparametric Estimation; III-posed In-verse Problems; Tikhonov Regularization; Endogeneity; InstrumentalVariable; Generalized Method of Moments; Subsampling; Engelcurve. (search for similar items in EconPapers)
JEL-codes: C13 C14 C15 D12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2006-05, Revised 2006-11
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp0630
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