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A note on the representative adaptive learning algorithm

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  • Berardi, Michele
  • Galimberti, Jaqueson K.

Abstract

We compare forecasts from different adaptive learning algorithms and calibrations applied 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 performance both in terms of forecasting accuracy and in matching (future) survey forecasts.

Suggested Citation

  • Berardi, Michele & Galimberti, Jaqueson K., 2014. "A note on the representative adaptive learning algorithm," Economics Letters, Elsevier, vol. 124(1), pages 104-107.
    • Handle: RePEc:eee:ecolet:v:124:y:2014:i:1:p:104-107
    DOI: 10.1016/j.econlet.2014.04.028
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    Citations

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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. Panovska, Irina & Ramamurthy, Srikanth, 2022. "Decomposing the output gap with inflation learning," Journal of Economic Dynamics and Control, Elsevier, vol. 136(C).
    6. 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.
    7. 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.
    8. 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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