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• W3041599977 abstract "Abstract This paper proposes a novel interpretation of the constant gain learning algorithm through a probabilistic setting with Bayesian updating. The underlying process for the variable being estimated is not specified a priori through a parametric model, and only its probabilistic structure is defined. Such framework allows to understand the gain coefficient in the learning algorithm in terms of the probability of changes in the estimated variable. On the basis of this framework, I assess the range of values commonly used in the macroeconomic empirical literature in terms of the implied probabilities of changes in the estimated variables." @default.
• W3041599977 created "2020-07-16" @default.
• W3041599977 creator A5052223545 @default.
• W3041599977 date "2020-07-09" @default.
• W3041599977 modified "2023-09-26" @default.
• W3041599977 title "A probabilistic interpretation of the constant gain learning algorithm" @default.
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• W3041599977 doi "https://doi.org/10.1111/boer.12256" @default.
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