Learning under signal-to-noise ratio uncertainty

Alex Ilek

Studies in Nonlinear Dynamics & Econometrics, 2013, vol. 17, issue 1, 47-83

Abstract: The paper presents an alternative real time adaptive learning algorithm in the presence of signal-to-noise ratio uncertainty. The main innovation of this algorithm is that it uses a gain which is determined within the model: it continuously depends on the extent of misevaluation of parameters embedded in the forecast error. We show that in the presence of signal-to-noise ratio misevaluation, the usage of the proposed learning algorithm is a significant improvement on the Kalman Filter learning algorithm. In a full information case, the Kalman Filter learning algorithm is still the optimal tool.

Keywords: adaptive learning; endogenous gain; Kalman Filter; parameter misevaluation index; signal-to-noise ratio (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1515/snde-2012-0046

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