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High Dimensional Semiparametric Moment Restriction Models

Chaohua Dong, Jiti Gao () and Oliver Linton ()

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

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-identification 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. [17].

Keywords: Generalized method of moments; high dimensional models; moment restriction; over-identification; penalization; sieve method; sparsity (search for similar items in EconPapers)
JEL-codes: C12 C14 C22 C30 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ore
Date: 2018-11-29
Note: obl20
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Related works:
Working Paper: High dimensional semiparametric moment restriction models (2018) Downloads
Working Paper: High dimensional semiparametric moment restriction models (2018) Downloads
Working Paper: High dimensional semiparametric moment restriction models (2018) Downloads
Working Paper: High dimensional semiparametric moment restriction models (2017) Downloads
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