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

Author

Listed:
  • Jooyoung Cha

    (Vanderbilt University)

  • Harold D. Chiang

    (University of Wisconsin)

  • Yuya Sasaki

    (Department of Economics, Vanderbilt University)

Programming Language

Stata

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).

Suggested Citation

  • Jooyoung Cha & Harold D. Chiang & Yuya Sasaki, 2025. "OGA: Stata module to perform estimation and inference for high-dimensional regressions without imposing the sparsity restriction," Statistical Software Components S459456, Boston College Department of Economics.
    • Handle: RePEc:boc:bocode:s459456
    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.
    as

    Download full text from publisher

    File URL: http://fmwww.bc.edu/repec/bocode/o/oga.ado
    File Function: program code
    Download Restriction: no
    File URL: http://fmwww.bc.edu/repec/bocode/o/oga.sthlp
    File Function: help file
    Download Restriction: no
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