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A loss discounting framework for model averaging and selection in time series models

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  • Bernaciak, Dawid
  • Griffin, Jim E.

Abstract

We introduce a loss discounting framework for model and forecast combination, which generalises and combines Bayesian model synthesis and generalized Bayes methodologies. We use a loss function to score the performance of different models and introduce a multilevel discounting scheme that allows for a flexible specification of the dynamics of the model weights. This novel and simple model combination approach can be easily applied to large-scale model averaging/selection, handle unusual features such as sudden regime changes and be tailored to different forecasting problems. We compare our method to established and state-of-the-art methods for several macroeconomic forecasting examples. The proposed method offers an attractive, computationally efficient alternative to the benchmark methodologies and often outperforms more complex techniques.

Suggested Citation

  • Bernaciak, Dawid & Griffin, Jim E., 2024. "A loss discounting framework for model averaging and selection in time series models," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1721-1733.
  • Handle: RePEc:eee:intfor:v:40:y:2024:i:4:p:1721-1733
    DOI: 10.1016/j.ijforecast.2024.03.001
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    1. Tony Chernis & Gary Koop & Emily Tallman & Mike West, 2024. "Decision Synthesis in Monetary Policy," Staff Working Papers 24-30, Bank of Canada.

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