Adaptive Learning in Practice
Eva Carceles-Poveda () and
Chryssi Giannitsarou ()
No 5627, CEPR Discussion Papers from C.E.P.R. Discussion Papers
We analyse some practical aspects of implementing adaptive learning in the context of forward-looking linear models. In particular, we focus on how to set initial conditions for three popular algorithms, namely recursive least squares, stochastic gradient and constant gain learning. We propose three ways of initializing, one that uses randomly generated data, a second that is ad-hoc and a third that uses an appropriate distribution. We illustrate, via standard examples, that the behaviour and evolution of macroeconomic variables not only depend on the learning algorithm, but on the initial conditions as well. Furthermore, we provide a computing toolbox for analysing the quantitative properties of dynamic stochastic macroeconomic models under adaptive learning.
Keywords: adaptive learning; computational methods; least square estimations; short-run dynamics (search for similar items in EconPapers)
JEL-codes: C63 D83 E10 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-evo and nep-mac
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Journal Article: Adaptive learning in practice (2007)
Working Paper: Adaptive Learning in Practice (2004)
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