Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230730
Authors: 
Davies, Laurie
Year of Publication: 
2018
Series/Report no.: 
IRTG 1792 Discussion Paper No. 2018-019
Abstract: 
Given data y and k covariates xj one problem in linear regression is to decide which if any of the covariates to include when regressing the dependent variable y on the covariates xj . In this paper three such methods, lasso, knockoff and Gaussian covariates are compared using simulations and real data. The Gaussian covariate method is based on exact probabilities which are valid for all y and xj making it model free. Moreover the probabilities agree with those based on the F-distribution for the standard linear model with i.i.d. Gaussian errors. It is conceptually, mathematically and algorithmically very simple, it is very fast and makes no use of simulations. It outperforms lasso and knockoff in all respects by a considerable margin.
JEL: 
C00
Document Type: 
Working Paper

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