OGA: Stata module to perform estimation and inference for high-dimensional regressions without imposing the sparsity restriction

Jooyoung Cha (), Harold Chiang and Yuya Sasaki
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Jooyoung Cha: Vanderbilt University

Statistical Software Components from Boston College Department of Economics

Abstract: oga performs estimation and inference for high-dimensional regression models without imposing a sparsity assumption, based on the methodology of Cha, Chiang, and Sasaki. The estimation procedure combines the orthogonal greedy algorithm (OGA), the high-dimensional Akaike information criterion (HDAIC), and double/debiased machine learning (DML).

Language: Stata
Requires: Stata version 14
Keywords: high-dimensional regression; orthogonal greedy algorithm; high-dimensional Akaike information criterion; double/debiased machine learning (search for similar items in EconPapers)
Date: 2025-05-12
Note: This module should be installed from within Stata by typing "ssc install oga". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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http://fmwww.bc.edu/repec/bocode/o/oga.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/o/oga.sthlp help file (text/plain)

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