Premium
A probabilistic interpretation of the constant gain learning algorithm
Author(s) -
Berardi Michele
Publication year - 2020
Publication title -
bulletin of economic research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.227
H-Index - 29
eISSN - 1467-8586
pISSN - 0307-3378
DOI - 10.1111/boer.12256
Subject(s) - probabilistic logic , a priori and a posteriori , variable (mathematics) , constant (computer programming) , interpretation (philosophy) , range (aeronautics) , parametric statistics , basis (linear algebra) , computer science , algorithm , mathematics , econometrics , bayesian probability , wake sleep algorithm , mathematical optimization , artificial intelligence , statistics , unsupervised learning , generalization error , mathematical analysis , philosophy , materials science , geometry , epistemology , composite material , programming language
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.