Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/189751 
Year of Publication: 
2018
Series/Report no.: 
cemmap working paper No. CWP35/18
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
This chapter presents key concepts and theoretical results for analyzing estimation and inference in high-dimensional models. High-dimensional models are characterized by having a number of unknown parameters that is not vanishingly small relative to the sample size. We first present results in a framework where estimators of parameters of interest may be represented directly as approximate means. Within this context, we review fundamental results including high-dimensional central limit theorems, bootstrap approximation of high-dimensional limit distributions, and moderate deviation theory. We also review key concepts underlying inference when many parameters are of interest such as multiple testing with family-wise error rate or false discovery rate control. We then turn to a general high-dimensional minimum distance framework with a special focus on generalized method of moments problems where we present results for estimation and inference about model parameters. The presented results cover a wide array of econometric applications, and we discuss several leading special cases including high-dimensional linear regression and linear instrumental variables models to illustrate the general results.
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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