Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/192891 
Year of Publication: 
2018
Series/Report no.: 
Working Paper No. 282
Publisher: 
University of Zurich, Department of Economics, 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.
Subjects: 
Model selection
sparsity
dense functionals
hypothesis testing
sensitivity analysis
JEL: 
C12
C51
C55
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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