Bayesian Forecasting in Economics and Finance: A Modern Review

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Tác giả: David T Frazier, Florian Huber, Gary Koop, Ruben Loaiza-Maya, John Maheu, Worapree Maneesoonthorn, Gael M Martin, Didier Nibbering, Anastasios Panagiotelis

Ngôn ngữ: eng

Ký hiệu phân loại: 923.3 *Persons in economics

Thông tin xuất bản: 2022

Mô tả vật lý:

Bộ sưu tập: Metadata

ID: 196192

 Comment: The paper is now published online at: https://doi.org/10.1016/j.ijforecast.2023.05.002The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem -- model, parameters, latent states -- is able to be quantified explicitly, and factored into the forecast distribution via the process of integration or averaging. Allied with the elegance of the method, Bayesian forecasting is now underpinned by the burgeoning field of Bayesian computation, which enables Bayesian forecasts to be produced for virtually any problem, no matter how large, or complex. The current state of play in Bayesian forecasting in economics and finance is the subject of this review. The aim is to provide the reader with an overview of modern approaches to the field, set in some historical context
  and with sufficient computational detail given to assist the reader with implementation.
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