Working Paper: CEPR ID: DP5627
Authors: Eva Carceles-Poveda; Chryssi Giannitsarou
Abstract: 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
JEL Codes: C63; D83; E10
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| learning algorithm employed (C45) | dynamics of macroeconomic variables (E19) |
| initial conditions set for algorithms (C61) | dynamics of macroeconomic variables (E19) |
| initial conditions (C62) | evolution of capital and other macroeconomic variables (E19) |
| initial conditions (C62) | learning dynamics (C69) |
| different algorithms (C45) | volatility in capital and consumption (E21) |
| initial conditions (C62) | short-run dynamics (C69) |