Adaptive nonparametric instrumental variables estimation: Empirical choice of the regularization parameter

Joel L. Horowitz

Journal of Econometrics, 2014, vol. 180, issue 2, 158-173

Abstract: In nonparametric instrumental variables estimation, the mapping that identifies the function of interest, g, is discontinuous and must be regularized to permit consistent estimation. The optimal regularization parameter depends on population characteristics that are unknown in applications. This paper presents a theoretically justified empirical method for choosing the regularization parameter in series estimation. The method adapts to the unknown smoothness of g and other unknown functions. The resulting estimator of g converges at least as fast as the optimal rate multiplied by (logn)1/2. The asymptotic integrated mean-square error (AIMSE) of the estimator is within a specified factor of the optimal AIMSE.

Keywords: Ill-posed inverse problem; Regularization; Series estimation; Nonparametric estimation (search for similar items in EconPapers)
JEL-codes: C13 C14 C21 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:180:y:2014:i:2:p:158-173

DOI: 10.1016/j.jeconom.2014.03.006

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