Arbeitspapier
Smoothing-based initialization for learning-to-forecast algorithms
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: KOF Working Papers ; No. 425
- Klassifikation
-
Wirtschaft
Computational Techniques; Simulation Modeling
Expectations; Speculations
Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
- Thema
-
learning algorithms
initialization
smoothing
expectations
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Berardi, Michele
Galimberti, Jaqueson K.
- Ereignis
-
Veröffentlichung
- (wer)
-
ETH Zurich, KOF Swiss Economic Institute
- (wo)
-
Zurich
- (wann)
-
2017
- DOI
-
doi:10.3929/ethz-a-010820132
- Handle
- Letzte Aktualisierung
-
20.09.2024, 08:22 MESZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Berardi, Michele
- Galimberti, Jaqueson K.
- ETH Zurich, KOF Swiss Economic Institute
Entstanden
- 2017
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