Computations for constrained linear models

Andrew Ronald Gallant, Thomas M. Gerig

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

The article presents an algorithm for linear regression computations subject to linear parametric equality constraints, linear parametric inequality constraints, or a mixture of the two. No rank conditions are imposed on the regression specification or the constraint specification. The algorithm requires a full Moore-Penrose g-inverse which entails extra computational effort relative to other orthonormalization type algorithms. In exchange, auxiliary statistical information is generated: feasibility of a set of constraints may be checked, estimability of a linear parametric function may be checked, and bias and variance may be decomposed by source.

Original languageEnglish (US)
Pages (from-to)59-84
Number of pages26
JournalJournal of Econometrics
Volume12
Issue number1
DOIs
StatePublished - Jan 1 1980

Fingerprint

Linear Model
Estimability
Specification
Specifications
Equality Constraints
Inequality Constraints
Linear regression
Regression
Inequality constraints
Equality
Penrose
Statistical Information
Linear Regression
Computational

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

Cite this

Gallant, Andrew Ronald ; Gerig, Thomas M. / Computations for constrained linear models. In: Journal of Econometrics. 1980 ; Vol. 12, No. 1. pp. 59-84.
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Computations for constrained linear models. / Gallant, Andrew Ronald; Gerig, Thomas M.

In: Journal of Econometrics, Vol. 12, No. 1, 01.01.1980, p. 59-84.

Research output: Contribution to journalArticle

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