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The Oxford Handbook of Economic Forecasting


  • Clements, Michael P.
    (University of Warwick)

  • Hendry, David F.
    (University of Oxford)


This Handbook provides up-to-date coverage of both new developments and well-established fields in the sphere of economic forecasting. The chapters are written by world experts in their respective fields, and provide authoritative yet accessible accounts of the key concepts, subject matter and techniques in a number of diverse but related areas. It covers the ways in which the availability of ever more plentiful data and computational power have been used in forecasting, either in terms of the frequency of observations, the number of variables, or the use of multiple data vintages. Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained; the volume provides explanations and critiques of these developments. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models, as well as models for handling data observed at mixed frequencies, high-frequency data, multiple data vintages, and methods for forecasting when there are structural breaks, and how breaks might be forecast. Also covered are areas which are less commonly associated with economic forecasting, such as climate change, health economics, long-horizon growth forecasting, and political elections. Econometric forecasting has important contributions to make in these areas, as well as their developments informing the mainstream. In the early 21st century, climate change and the forecasting of health expenditures and population are topics of pressing importance. Available in OSO:

Suggested Citation

  • Clements, Michael P. & Hendry, David F. (ed.), 2011. "The Oxford Handbook of Economic Forecasting," OUP Catalogue, Oxford University Press, number 9780195398649.
  • Handle: RePEc:oxp:obooks:9780195398649

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    References listed on IDEAS

    1. Gordon,Robert J., 2004. "Productivity Growth, Inflation, and Unemployment," Cambridge Books, Cambridge University Press, number 9780521531429, March.
    2. Dale W. Jorgenson, 2001. "Information Technology and the U.S. Economy," American Economic Review, American Economic Association, vol. 91(1), pages 1-32, March.
    3. Gordon,Robert J., 2004. "Productivity Growth, Inflation, and Unemployment," Cambridge Books, Cambridge University Press, number 9780521800082, March.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Romeo Ionescu, 2012. "The Economic Recovery across the EU vs the Global Crisis," EuroEconomica, Danubius University of Galati, issue 2(31), pages 30-39, May.
    2. Bialowolski, Piotr & Kuszewski, Tomasz & Witkowski, Bartosz, 2015. "Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 9, pages 1-37.
    3. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Common Drifting Volatility in Large Bayesian VARs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 375-390, July.
    4. Kenneth W Clements & Grace Gao, 2013. "A Multi-Market Approach to Measuring the Cycle," Economics Discussion / Working Papers 13-16, The University of Western Australia, Department of Economics.
    5. Johanna Posch & Fabio Rumler, 2015. "Semi‐Structural Forecasting of UK Inflation Based on the Hybrid New Keynesian Phillips Curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 145-162, March.
    6. repec:cml:moneta:v:iii:y:2015:i:1:p:25-69 is not listed on IDEAS
    7. Marczak, Martyna & Proietti, Tommaso, 2016. "Outlier detection in structural time series models: The indicator saturation approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 180-202.
    8. Kőrösi, Gábor, 2016. "A lány továbbra is szolgál..
      [Modelling and econometrics]
      ," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(6), pages 647-667.
    9. Ericsson, Neil R., 2017. "Economic forecasting in theory and practice: An interview with David F. Hendry," International Journal of Forecasting, Elsevier, vol. 33(2), pages 523-542.
    10. David Hendry & Grayham E. Mizon, 2016. "Improving the Teaching of Econometrics," Economics Series Working Papers 785, University of Oxford, Department of Economics.
    11. Ricardo Reis, 2017. "Is Something Really Wrong with Macroeconomics?," CESifo Working Paper Series 6446, CESifo Group Munich.
    12. Carlos A. Medel, 2015. "Inflation Dynamics and the Hybrid New Keynesian Phillips Curve: The Case of Chile," Monetaria, Centro de Estudios Monetarios Latinoamericanos, vol. 0(1), pages 25-69, january-j.
    13. Medel, Carlos A., 2015. "Forecasting Inflation with the Hybrid New Keynesian Phillips Curve: A Compact-Scale Global VAR Approach," MPRA Paper 67081, University Library of Munich, Germany.
    14. Fritz Breuss, 2016. "Would DSGE Models have Predicted the Great Recession in Austria?," WIFO Working Papers 530, WIFO.
    15. Meriküll, Jaanika & Eamets, Raul & Humal, Katrin & Espenberg, Kerly, 2012. "Power without manpower: Forecasting labour demand for Estonian energy sector," Energy Policy, Elsevier, vol. 49(C), pages 740-750.
    16. Yuxuan Huang, 2016. "Forecasting the USD/CNY Exchange Rate under Different Policy Regimes," Working Papers 2016-001, The George Washington University, Department of Economics, Research Program on Forecasting.

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