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Targeted undersmoothing

Christian Hansen (), Damian Kozbur and Sanjog Misra

No 282, ECON - Working Papers from Department of Economics - University of Zurich

Abstract: This paper proposes a post-model selection inference procedure, called targeted undersmoothing, designed to construct uniformly valid confidence sets for functionals of sparse high-dimensional models, including dense functionals that may depend on many or all elements of the high-dimensional parameter vector. The confidence sets are based on an initially selected model and two additional models which enlarge the initial model. By varying the enlargements of the initial model, one can also conduct sensitivity analysis of the strength of empirical conclusions to model selection mistakes in the initial model. We apply the procedure in two empirical examples: estimating heterogeneous treatment effects in a job training program and estimating profitability from an estimated mailing strategy in a marketing campaign. We also illustrate the procedure’s performance through simulation experiments.

Keywords: model selection; sparsity; dense functionals; hypothesis testing; sensitivity analysis (search for similar items in EconPapers)
JEL-codes: C12 C51 C55 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ore
Date: 2016-08, Revised 2018-04
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