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A Note on the Representative Adaptive Learning Algorithm

We compare forecasts from different adaptive learning algorithms and calibrations ap- plied to US real-time data on inflation and growth. We find that the Least Squares with constant gains adjusted to match (past) survey forecasts provides the best overall perfor- mance both in terms of forecasting accuracy and in matching (future) survey forecasts.

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File URL: http://dx.doi.org/10.3929/ethz-a-010131559
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Paper provided by KOF Swiss Economic Institute, ETH Zurich in its series KOF Working papers with number 14-356.

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Length: 21 pages
Date of creation: Apr 2014
Handle: RePEc:kof:wpskof:14-356
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  1. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
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  3. Stark, Tom & Croushore, Dean, 2002. "Forecasting with a real-time data set for macroeconomists," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 507-531, December.
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  8. Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, Elsevier.
  9. Andrew J. Patton & Allan Timmermann, 2011. "Predictability of Output Growth and Inflation: A Multi-Horizon Survey Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(3), pages 397-410, July.
  10. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
  11. James Bullard & Stefano Eusepi, 2005. "Did the Great Inflation Occur Despite Policymaker Commitment to a Taylor Rule?," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 324-359, April.
  12. James H. Stock & Mark W. Watson, 2003. "Has the Business Cycle Changed and Why?," NBER Chapters,in: NBER Macroeconomics Annual 2002, Volume 17, pages 159-230 National Bureau of Economic Research, Inc.
  13. Markiewicz, Agnieszka & Pick, Andreas, 2014. "Adaptive learning and survey data," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 685-707.
  14. Branch, William A. & Evans, George W., 2006. "A simple recursive forecasting model," Economics Letters, Elsevier, vol. 91(2), pages 158-166, May.
  15. James H. Stock & Mark W. Watson, 2003. "Has the business cycle changed?," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 9-56.
  16. Heinemann, Maik, 2000. "Convergence Of Adaptive Learning And Expectational Stability: The Case Of Multiple Rational-Expectations Equilibria," Macroeconomic Dynamics, Cambridge University Press, vol. 4(03), pages 263-288, September.
  17. Barucci, Emilio & Landi, Leonardo, 1997. "Least mean squares learning in self-referential linear stochastic models," Economics Letters, Elsevier, vol. 57(3), pages 313-317, December.
  18. Michele Berardi & Jaqueson K. Galimberti, 2012. "On the initialization of adaptive learning algorithms: A review of methods and a new smoothing-based routine," Centre for Growth and Business Cycle Research Discussion Paper Series 175, Economics, The Univeristy of Manchester.
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