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GTAP Resource #6152

"Comparing three penalty functions - Cross Entropy, Quadratic and Linear Loss – in SAM splitting applications"
by Britz, Wolfgang

We review three candidates for balancing data which enable to consider bounds, different priorities and unknown row or column totals and thus are useful for SAM splitting and balancing: Cross-Entropy, a Highest Posterior Density Estimator resulting in a quadratic loss penalty function and minimizing absolute differences, i.e. linear loss. The approaches are assessed first by a systematic Monte-Carlo experiment with known distribution of the errors. Here we find quite limited numerical differences between the Cross-Entropy and quadratic loss. The Cross-Entropy approach was however considerably slower than the other candidates. Second, we tested the three approaches for differently sized larger SAM split problems with unknown errors, considering here also besides CONOPT4 the specialized LP/QP solvers CPLEDX and GUROBI. Again, the differences in results between the quadratic loss and the Cross-Entropy approach were quite small while the quadratic loss problem could be extremely fast solved with the specialized QP solvers. However, they did not achieve the same accuracy as CONOPT4, while under linear loss, the specialized solvers are faster by around factor ten at a similar accuracy. We conclude that using linear loss in combination with a specialized solver or a quadratic loss approach are the most suitable candidates for larger SAM splitting / balancing problems.

JEL codes: C67 Input–Output Models, C63 Computational Techniques, C88 Other Computer Software

Keywords: Data balancing, SAM balancing, Highest Posterior Density, Cross Entropy

Resource Details (Export Citation) GTAP Keywords
Category: 2020 Conference Paper
Status: Published
By/In: Presented during the 23rd Annual Conference on Global Economic Analysis (Virtual Conference)
Date: 2020
Created: Britz, W. (5/5/2020)
Updated: Britz, W. (6/19/2020)
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