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Michele, Berardi ; Jaqueson, Galimberti. (2012) On the plausibility of adaptive learning in macroeconomics: A puzzling conflict in the choice of the representative algorithm.
In: Centre for Growth and Business Cycle Research Discussion Paper Series. RePEc:man:cgbcrp:177.
Full description at Econpapers
cites:
A Review of a priori comparisons between the algorithms Computational complexity: The SG algorithm requires a lower number of computations for a complete iteration of adaptation than the LS, given that this latter requires the inversion of the matrix of moments, an operation for which computational complexity grows exponentially with the number of regressors. To be specific, while an SG iteration requires only 2K +1 multiplications and 2K additions, a LS iteration requires K2 + 5K + 1 multiplications, K2 + 3K additions, and 1 division, with K standing for the number of regressors in xt (see Sayed, 2008, pps. 166, 200-201).
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