Macroeconomic forecasting using BVARs

Tools

Hauzenberger, Niko and Huber, Florian and Koop, Gary; Clements, Michael P. and Galvao, Ana Beatriz, eds. (2024) Macroeconomic forecasting using BVARs. In: Handbook of Research Methods and Applications on Macroeconomic Forecasting. Handbooks of Research Methods and Applications . Edward Elgar, Cheltenham, UK, pp. 15-42. ISBN 978 1 03531 005 0

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Abstract

Bayesian Vector Autoregressions (BVARs) have come a long way since the classic early work of Chris Sims and his co-authors, e.g., Sims (1980) and Doan et al. (1984), and have developed into one of the most popular tools for macroeconomic forecasting. The original Minnesota prior used in early work is still very popular, but a range of alternative priors have been proposed with various properties. In this chapter, we will discuss some of the many new VAR priors that have been proposed over the last decades and discuss their properties.

ORCID iDs

Hauzenberger, Niko, Huber, Florian and Koop, Gary ORCID logoORCID: https://orcid.org/0000-0002-6091-378X; Clements, Michael P. and Galvao, Ana Beatriz
  • Item type:Book Section
    ID code:87689
    Dates:
    Date
    Event
    26 November 2024
    Published
    14 August 2023
    Accepted
    Notes:This is a draft chapter/article. The final version is available in Handbook of Research Methods and Applications in Macroeconomic Forecasting edited by Michael P. Clements and Ana Beatriz Galvão, published in 2024, Edward Elgar Publishing Ltd https://doi.org/10.4337/9781035310050. It is deposited under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
    Subjects:Social Sciences > Economic Theory
    Department:Strathclyde Business School > Economics
    Depositing user: Pure Administrator
    Date deposited:20 Dec 2023 12:03
    Last modified:29 Nov 2024 14:48
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/87689
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