CLSPL2: Stata module providing Convex Least Squares Programming (CLSP) is a modular two-step estimator for solving underdetermined, ill-posed, or structurally constrained least-squares problems

Ilya Bolotov

Statistical Software Components from Boston College Department of Economics

Abstract: Convex Least Squares Programming (CLSP) is a modular two-step estimator for solving underdetermined, ill-posed, or structurally constrained least-squares problems. It combines pseudoinverse-based estimation with convex-programming correction methods inspired by Lasso, Ridge, and Elastic Net to ensure numerical stability, constraint enforcement, and interpretability. The current implementation limits the second step to the Ridge case by employing a second pseudoinverse estimation. The package also provides numerical stability analysis and CLSP-specific diagnostics, including partial R^2, normalized RMSE (NRMSE), bootstrap or Monte Carlo t-tests for mean NRMSE, and condition-number-based confidence bands.

Language: Stata
Requires: Stata version 15.1
Keywords: linear programming; least squares; pseudoinverse; singular value decomposition (search for similar items in EconPapers)
Date: 2026-05-07, Revised 2026-05-14
Note: This module should be installed from within Stata by typing "ssc install clspl2". 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/c/clspl2.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/c/clspl2.sthlp help file (text/plain)
http://fmwww.bc.edu/repec/bocode/l/lclspl2.mlib object code (application/x-stata)

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Software Item: CLSP: Stata module providing Convex Least Squares Programming (CLSP) is a modular two-step estimator for solving underdetermined, ill-posed, or structurally constrained least-squares problems (2026)
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