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A Note on the Representative Adaptive Learning Algorithm

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Abstract

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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  • Michele Bernardi & Jaqueson K. Galimberti, 2014. "A Note on the Representative Adaptive Learning Algorithm," KOF Working papers 14-356, KOF Swiss Economic Institute, ETH Zurich.
  • Handle: RePEc:kof:wpskof:14-356
    DOI: 10.3929/ethz-a-010131559
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    Cited by:

    1. Christina Strobach & Carin van der Cruijsen, 2015. "The formation of European inflation expectations: One learning rule does not fit all," DNB Working Papers 472, Netherlands Central Bank, Research Department.
    2. Berardi, Michele & Galimberti, Jaqueson K., 2017. "Empirical calibration of adaptive learning," Journal of Economic Behavior & Organization, Elsevier, vol. 144(C), pages 219-237.
    3. Jaqueson K. Galimberti, 2020. "Information weighting under least squares learning," CAMA Working Papers 2020-46, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Damjanovic, Tatiana & Girdėnas, Šarūnas & Liu, Keqing, 2015. "Stationarity of econometric learning with bounded memory and a predicted state variable," Economics Letters, Elsevier, vol. 130(C), pages 93-96.
    5. Galimberti, Jaqueson K., 2019. "An approximation of the distribution of learning estimates in macroeconomic models," Journal of Economic Dynamics and Control, Elsevier, vol. 102(C), pages 29-43.
    6. Berardi, Michele & Galimberti, Jaqueson K., 2017. "On the initialization of adaptive learning in macroeconomic models," Journal of Economic Dynamics and Control, Elsevier, vol. 78(C), pages 26-53.

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    More about this item

    Keywords

    Expectations; Learning algorithms; Forecasting; Learning-to-forecast; Least squares; Stochastic gradient;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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