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

No CWP69/18, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: We consider nonlinear moment restriction semiparametric models where both the dimension of the parameter vector and the number of restrictions are divergent with sample size and an unknown smooth function is involved. We propose an estimation method based on the sieve generalized method of moments (sieve GMM). We establish consistency and asymptotic normality for the estimated quantities when the number of parameters increases modestly with sample size. We also consider the case where the number of potential parameters/covariates is very large, i.e., increases rapidly with sample size, but the true model exhibits sparsity. We use a penalized sieve GMM approach to select the relevant variables, and establish the oracle property of our method in this case. We also provide new results for inference. We propose several new test statistics for the over-identi fication and establish their large sample properties. We provide a simulation study that shows the performance of our methodology. We also provide an application to modelling the effect of schooling on wages using data from the NLSY79 used by Carneiro et al.

Keywords: Generalized method of moments; high dimensional models; moment restriction; over-identi fication; penalization; sieve method; sparsity (search for similar items in EconPapers)
Date: 2018-12-04
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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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