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Directed graphs, information structure and forecast combinations: an empirical examination of US unemployment rates

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  • Zijun Wang

    (Private Enterprise Research Center, Texas A&M University, College Station, Texas, USA)

Abstract

Previous studies show that it is not always optimal to combine forecasts of alternative models. In this paper, we propose to use the recent advances in modeling directed acyclic graphs to study the issue of forecast combinations. In forecasting US unemployment rates, we demonstrate that the proposed procedure can be a useful tool for comparing information in rival forecasts and guiding the combination of individual forecasts. Copyright © 2009 John Wiley & Sons, Ltd

Suggested Citation

  • Zijun Wang, 2010. "Directed graphs, information structure and forecast combinations: an empirical examination of US unemployment rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(4), pages 353-366.
    • Handle: RePEc:jof:jforec:v:29:y:2010:i:4:p:353-366
    DOI: 10.1002/for.1128
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    1. Andreas Karatahansopoulos & Georgios Sermpinis & Jason Laws & Christian Dunis, 2014. "Modelling and Trading the Greek Stock Market with Gene Expression and Genetic Programing Algorithms," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(8), pages 596-610, December.
    2. Xiaojie Xu & Yun Zhang, 2022. "Contemporaneous causality among one hundred Chinese cities," Empirical Economics, Springer, vol. 63(4), pages 2315-2329, October.
    3. Michael H. Breitner & Christian Dunis & Hans-Jörg Mettenheim & Christopher Neely & Georgios Sermpinis & Georgios Sermpinis & Charalampos Stasinakis & Konstantinos Theofilatos & Andreas Karathanasopoul, 2014. "Inflation and Unemployment Forecasting with Genetic Support Vector Regression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(6), pages 471-487, September.

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