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Learning under signal-to-noise ratio uncertainty

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  • Ilek Alex

    (Research Division, Bank of Israel, Jerusalem 91007, Israel)

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.

Suggested Citation

  • Ilek Alex, 2013. "Learning under signal-to-noise ratio uncertainty," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(1), pages 47-83, February.
    • Handle: RePEc:bpj:sndecm:v:17:y:2013:i:1:p:47-83:n:5
    DOI: 10.1515/snde-2012-0046
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    References listed on IDEAS

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