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Adaptive Learning in Practice

  • Carceles-Poveda, Eva
  • Giannitsarou, Chryssi

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.

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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 5627.

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Date of creation: Apr 2006
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Handle: RePEc:cpr:ceprdp:5627
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