DP5627 Adaptive Learning in Practice

Author(s): Eva Carceles-Poveda , Chryssi Giannitsarou

Publication Date: April 2006

Keyword(s): adaptive learning , least square estimations , computational methods , short-run dynamics

JEL(s): C63 , D83 , E10

Programme Areas: International Macroeconomics

Link to this Page: www.cepr.org/pubs/dps/DP5627.asp

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.