On the initialization of adaptive learning algorithms: A review of methods and a new smoothing-based routine
AbstractWe provide a critical review on the methods previously adopted into the literature of learning and expectations in macroeconomics in order to initialize its underlying learning algorithms either for simulation or empirical purposes. We find that none of these methods is able to pass the sieve of both criteria of coherence to the algorithm long run behavior and of feasibility within the data availability restrictions for macroeconomics. We then propose a smoothing-based initialization routine, and show through simulations that our method meets both those criteria in exchange for a higher computational cost. A simple empirical application is also presented to demonstrate the relevance of initialization for beginning-of-sample inferences.
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Bibliographic InfoPaper provided by Economics, The Univeristy of Manchester in its series Centre for Growth and Business Cycle Research Discussion Paper Series with number 175.
Length: 35 pages
Date of creation: 2012
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- Berardi, Michele & Galimberti, Jaqueson K., 2014.
"A note on the representative adaptive learning algorithm,"
Elsevier, vol. 124(1), pages 104-107.
- Jaqueson Galimberti & Michele Berardi, 2014. "A Note on the Representative Adaptive Learning Algorithm," KOF Working papers 14-356, KOF Swiss Economic Institute, ETH Zurich.
- Agnieszka Markiewicz & Andreas Pick, 2013.
"Adaptive Learning and Survey Data,"
CDMA Working Paper Series
201305, Centre for Dynamic Macroeconomic Analysis.
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