Valid simultaneous inference in high-dimensional settings (with the HDM package for R)

Philipp Bach, Victor Chernozhukov and Martin Spindler
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Philipp Bach: Institute for Fiscal Studies
Martin Spindler: Institute for Fiscal Studies

No CWP30/19, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: Due to the increasing availability of high-dimensional empirical applications in many research disciplines, valid simultaneous inference becomes more and more important. For instance, high-dimensional settings might arise in economic studies due to very rich data sets with many potential covariates or in the analysis of treatment heterogeneities. Also the evaluation of potentially more complicated (non-linear) functional forms of the regression relationship leads to many potential variables for which simultaneous inferential statements might be of interest. Here we provide a review of classical and modern methods for simultaneous inference in (high-dimensional) settings and illustrate their use by a case study using the R package hdm. The R package hdm implements valid joint powerful and e?cient hypothesis tests for a potentially large number of coe?cients as well as the construction of simultaneous con?dence intervals and, therefore, provides useful methods to perform valid post-selection inference based on the LASSO. R and the package hdm are open-source software projects and can be freely downloaded from CRAN: http://cran.r-project.org.

Date: 2019-06-12
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Working Paper: Valid Simultaneous Inference in High-Dimensional Settings (with the hdm package for R) (2018)
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