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

  • Zijun Wang

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

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    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

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    File URL: http://hdl.handle.net/10.1002/for.1128
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    Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

    Volume (Year): 29 (2010)
    Issue (Month): 4 ()
    Pages: 353-366

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    Handle: RePEc:jof:jforec:v:29:y:2010:i:4:p:353-366
    DOI: 10.1002/for.1128
    Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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    1. Zijun Wang & David A. Bessler, 2006. "Price and quantity endogeneity in demand analysis: evidence from directed acyclic graphs," Agricultural Economics, International Association of Agricultural Economists, vol. 34(1), pages 87-95, 01.
    2. Selva Demiralp & Kevin D. Hoover, 2003. "Searching for the Causal Structure of a Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 745-767, December.
    3. Hoover, Kevin D., 2005. "Automatic Inference Of The Contemporaneous Causal Order Of A System Of Equations," Econometric Theory, Cambridge University Press, vol. 21(01), pages 69-77, February.
    4. Granger, Clive W. J., 2003. "Some aspects of causal relationships," Journal of Econometrics, Elsevier, vol. 112(1), pages 69-71, January.
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    7. Min, C.K. & Zellner, A., 1992. ""Bayesian and Non-Bayesian Methods for Combining Models and Forecasts with Applications to Forecasting International Growth Rates"," Papers 90-92-23, California Irvine - School of Social Sciences.
    8. David Hendry & Michael P. Clements, 2001. "Pooling of Forecasts," Economics Papers 2002-W9, Economics Group, Nuffield College, University of Oxford.
    9. Dean Croushore & Tom Stark, 2003. "A Real-Time Data Set for Macroeconomists: Does the Data Vintage Matter?," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 605-617, August.
    10. Steffen L. Lauritzen & Thomas S. Richardson, 2002. "Chain graph models and their causal interpretations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 321-348.
    11. David Harvey & Paul Newbold, 2000. "Tests for multiple forecast encompassing," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 471-482.
    12. Swanson, Norman R & Zeng, Tian, 2001. "Choosing among Competing Econometric Forecasts: Regression-Based Forecast Combination Using Model Selection," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(6), pages 425-40, September.
    13. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    14. Bunn, Derek W., 1985. "Statistical efficiency in the linear combination of forecasts," International Journal of Forecasting, Elsevier, vol. 1(2), pages 151-163.
    15. Yock Y. Chong & David F. Hendry, 1986. "Econometric Evaluation of Linear Macro-Economic Models," Review of Economic Studies, Oxford University Press, vol. 53(4), pages 671-690.
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