Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/192893 
Authors: 
Year of Publication: 
2018
Series/Report no.: 
Working Paper No. 284
Publisher: 
University of Zurich, Department of Economics, Zurich
Abstract: 
This paper studies nonparametric series estimation and inference for the effect of a single variable of interest x on an outcome y in the presence of potentially high-dimensional conditioning variables z. The context is an additively separable model E[y, z] = g0(x) + h0(z). The model is high-dimensional in the sense that the series of approximating functions for h0(z) can have more terms than the sample size, thereby allowing z to have potentially very many measured characteristics. The model is required to be approximately sparse: h0(z) can be approximated using only a small subset of series terms whose identities are unknown. This paper proposes an estimation and inference method for g0(x) called Post-Nonparametric Double Selection which is a generalization of Post-Double Selection. Standard rates of convergence and asymptotic normality for the estimator are shown to hold uniformly over a large class of sparse data generating processes. A simulation study illustrates finite sample estimation properties of the proposed estimator and coverage properties of the corresponding confidence intervals. Finally, an empirical application estimating convergence in GDP in a country-level crosssection demonstrates the practical implementation of the proposed method.
Subjects: 
Additive nonparametric models
high-dimensional sparse regression
inference under imperfect model selection
JEL: 
C1
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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