High dimensional semiparametric moment restriction models

Chaohua Dong (), Jiti Gao and Oliver Linton

No 17/17, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: Moment restriction semiparametric models, where both the dimension of parameter and the number of restrictions are divergent and an unknown function is involved, are studied using the generalized method of moments (GMM) and sieve method dealing with the nonparametric parameter. The consistency and normality for the GMM estimators are established. Meanwhile, a new test statistic is proposed for overidentification issue. Numerical examples are used to verify the established theory.

Keywords: Generalized method of moments; high dimensional models; moment restriction; over-identification; sieve method. (search for similar items in EconPapers)
JEL-codes: C12 C14 C22 C30 (search for similar items in EconPapers)
Pages: 43
Date: 2017
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: High dimensional semiparametric moment restriction models (2023)
Working Paper: High Dimensional Semiparametric Moment Restriction Models (2018)
Working Paper: High dimensional semiparametric moment restriction models (2018)
Working Paper: High dimensional semiparametric moment restriction models (2018)
Working Paper: High dimensional semiparametric moment restriction models (2018)
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