High dimensional semiparametric moment restriction models

Chaohua Dong, Jiti Gao and Oliver Linton
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Chaohua Dong: Institute for Fiscal Studies and Southwestern University of Finance and Economics, China
Jiti Gao: Institute for Fiscal Studies

No CWP04/18, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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 over-identi fication issue, which also is workable for the traditional moment restriction models. In addition, the potential sparsity under our setting is investigated via the combination of GMM methodology and penalty function approach. Numerical examples are used to verify the established theory.

Keywords: Generalized method of moments; high dimensional models; moment restriction; over-identifi cation; sieve method; sparsity (search for similar items in EconPapers)
Date: 2018-01-10
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Citations: View citations in EconPapers (3)

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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 (2017)
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