Adaptive learning in practice

Abstract

We analyze practical aspects of implementing adaptive learning in the context of forward looking linear models. 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, one that is ad hoc and one that uses an appropriate distribution. We illustrate via standard examples, that the behavior of macroeconomic variables not only depends on the learning algorithm, but on the initial conditions as well. Furthermore, we provide a computing toolbox for analyzing the quantitative properties of dynamic stochastic macroeconomic models under adaptive learning.