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Paring 3SLS Calculations Down to Manageable Proportions

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  • Belsley, David A

Abstract

The standard computational formula for the three-stage least-squares estimator is a daunting affair even for modest sized systems of equations. Through the use of the QR decomposition, however, these computations can be substantially reduced in size, removing the order of "T"(number of observations) from the relevant dimensions. This produces a set of calculations and memory requirements far more accommodating to all users of 3SLS (three-stage least-squares), but particularly to those who may wish to include this estimator in their home-made arsenal without having to engage in special programming techniques. Citation Copyright 1992 by Kluwer Academic Publishers.

Suggested Citation

  • Belsley, David A, 1992. "Paring 3SLS Calculations Down to Manageable Proportions," Computer Science in Economics & Management, Kluwer;Society for Computational Economics, vol. 5(3), pages 157-169, August.
    • Handle: RePEc:kap:csecmg:v:5:y:1992:i:3:p:157-69
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    Cited by:

    1. Kontoghiorghes, Erricos J & Dinenis, Elias, 1997. "Computing 3SLS Solutions of Simultaneous Equation Models with a Possible Singular Variance-Covariance Matrix," Computational Economics, Springer;Society for Computational Economics, vol. 10(3), pages 231-250, August.
    2. Cameron McIntosh, 2014. "The presence of an error term does not preclude causal inference in regression: a comment on Krause (2012)," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(1), pages 243-250, January.
    3. Foschi, Paolo & Belsley, David A. & Kontoghiorghes, Erricos J., 2003. "A comparative study of algorithms for solving seemingly unrelated regressions models," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 3-35, October.
    4. Mircea I. Cosbuc & Cristian Gatu & Ana Colubi & Erricos John Kontoghiorghes, 2017. "A Generalized Singular Value Decomposition Strategy for Estimating the Block Recursive Simultaneous Equations Model," Computational Economics, Springer;Society for Computational Economics, vol. 50(3), pages 503-515, October.

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