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SMOOTHING-BASED INITIALIZATION FOR LEARNING-TO-FORECAST ALGORITHMS

Published online by Cambridge University Press:  23 June 2017

Michele Berardi  and
Jaqueson K. Galimberti
Michele Berardi
Affiliation:
University of Manchester
Jaqueson K. Galimberti*
Affiliation:
ETH Zurich
*
Address correspondence to: Jaqueson K. Galimberti, KOF Swiss Economic Institute, ETH Zurich, LEE G 116, Leonhardstrasse 21, 8092 Zurich, Switzerland; e-mail: galimberti@kof.ethz.ch.
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Abstract

Under adaptive learning, recursive algorithms are proposed to represent how agents update their beliefs over time. For applied purposes, these algorithms require initial estimates of agents perceived law of motion. Obtaining appropriate initial estimates can become prohibitive within the usual data availability restrictions of macroeconomics. To circumvent this issue, we propose a new smoothing-based initialization routine that optimizes the use of a training sample of data to obtain initials consistent with the statistical properties of the learning algorithm. Our method is generically formulated to cover different specifications of the learning mechanism, such as the least-squares and the stochastic gradient algorithms. Using simulations, we show that our method is able to speed up the convergence of initial estimates in exchange for a higher computational cost.

Keywords

Learning AlgorithmsInitializationSmoothingExpectations

Information

Type
Articles
Information
Macroeconomic Dynamics , Volume 23 , Issue 3 , April 2019 , pp. 1008 - 1023
Copyright
Copyright © Cambridge University Press 2017 

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Footnotes

An earlier version of this paper was presented at the 2013 Computing in Economics and Finance conference in Vancouver. We thank to our discussants for helpful comments. We also gratefully acknowledge the comments provided by one Associate Editor and two referees. Finally, we thank the Editor Professor William A. Barnett for the quick responsiveness and handling of our submission. Any remaining errors are ours.

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