Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/211156 
Year of Publication: 
2019
Series/Report no.: 
cemmap working paper No. CWP63/19
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
We present a general framework for studying regularized estimators; such estimators are pervasive in estimation problems wherein "plug-in" type estimators are either ill-defined or ill-behaved. Within this framework, we derive, under primitive conditions, consistency and a generalization of the asymptotic linearity property. We also provide data-driven methods for choosing tuning parameters that, under some conditions, achieve the aforementioned properties. We illustrate the scope of our approach by studying a wide range of applications, revisiting known results and deriving new ones.
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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