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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.
Authors
Michael Jansson
Demian Pouzo
Working Paper details
- DOI
- 10.1920/wp.cem.2019.6319
- Publisher
- The IFS
Suggested citation
Jansson, M and Pouzo, D. (2019). Towards a general large sample theory for regularized estimators. London: The IFS. Available at: https://ifs.org.uk/publications/towards-general-large-sample-theory-regularized-estimators (accessed: 19 April 2024).
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