Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/162241
Authors: 
Berardi, Michele
Galimberti, Jaqueson K.
Year of Publication: 
2017
Series/Report no.: 
KOF Working Papers 425
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.
Subjects: 
learning algorithms
initialization
smoothing
expectations
JEL: 
C63
D84
E37
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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