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On the initialization of adaptive learning algorithms: A review of methods and a new smoothing-based routine

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  • Michele Berardi
  • Jaqueson K. Galimberti

Abstract

We 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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File URL: http://www.socialsciences.manchester.ac.uk/medialibrary/cgbcr/discussionpapers/dpcgbcr175.pdf
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Bibliographic Info

Paper provided by Economics, The Univeristy of Manchester in its series Centre for Growth and Business Cycle Research Discussion Paper Series with number 175.

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Length: 35 pages
Date of creation: 2012
Date of revision:
Handle: RePEc:man:cgbcrp:175

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References

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  20. Michele Berardi & Jaqueson K. Galimberti, 2012. "A note on exact correspondences between adaptive learning algorithms and the Kalman filter," Centre for Growth and Business Cycle Research Discussion Paper Series 170, Economics, The Univeristy of Manchester.
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Citations

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Cited by:
  1. Michele Berardi & Jaqueson K. Galimberti, 2012. "On the plausibility of adaptive learning in macroeconomics: A puzzling conflict in the choice of the representative algorithm," Centre for Growth and Business Cycle Research Discussion Paper Series 177, Economics, The Univeristy of Manchester.
  2. Agnieszka Markiewicz & Andreas Pick, 2013. "Adaptive Learning and Survey Data," CDMA Working Paper Series 201305, Centre for Dynamic Macroeconomic Analysis.

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