TMPINV: Stata module solving Tabular Matrix Problems via Pseudoinverse Estimation (TMPinv) is a modular two-step estimator for solving underdetermined, ill-posed, or structurally constrained allocation problems using least-squares and convex optimization

Ilya Bolotov

Statistical Software Components from Boston College Department of Economics

Abstract: The Tabular Matrix Problems via Pseudoinverse Estimation (TMPinv) is a two-stage estimation method that reformulates structured table-based systems - such as allocation problems, transaction matrices, and input–output tables - as structured least-squares problems. Based on the Convex Least Squares Programming (CLSP) framework, TMPinv solves systems with row and column constraints, block structure, and optionally reduced dimensionality by (1) constructing a canonical constraint form and applying a pseudoinverse-based projection, followed by (2) a convex-programming refinement stage to improve fit, coherence, and regularization (e.g., via Lasso, Ridge, or Elastic Net).

Language: Stata
Requires: Stata version 16 and Python packages pytmpinv, numpy, cvxppy
Keywords: linear programming; least squares; pseudoinverse; singular value decomposition (search for similar items in EconPapers)
Date: 2022-10-05, Revised 2026-01-20
Note: This module should be installed from within Stata by typing "ssc install tmpinv". 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/t/tmpinv.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/t/tmpinv.sthlp help file (text/plain)

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