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The potential of a small model

Author

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  • Adam Elbourne

    () (CPB Netherlands Bureau for Economic Policy Analysis)

  • Coen Teulings

    (CPB Netherlands Bureau for Economic Policy Analysis)

Abstract

This CPB Discussion Paper highlights potential uses of simple, small models where large traditional models are less flexible. (updated 22/12/2011). We run a number of experiments with a small two variable VAR model of GDP growth and unemployment with both quarterly and yearly data. We compare the forecasts of these simple models with the published forecasts of the CPB and we conclude that there is not much di erence. We then show how easy it is to evaluate the usefulness of a given variable for forecasting by extending the model to include world trade. Perfect knowledge of future world trade growth would help considerably but is obviously not available at the time the forecasts were made. The available world trade data doesn't improve the forecasts. Finally we also show how quick and exible measures of the output gap can be constructed.

Suggested Citation

  • Adam Elbourne & Coen Teulings, 2011. "The potential of a small model," CPB Discussion Paper 193, CPB Netherlands Bureau for Economic Policy Analysis.
  • Handle: RePEc:cpb:discus:193
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    References listed on IDEAS

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    1. Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez & Thomas J. Sargent & Mark W. Watson, 2007. "ABCs (and Ds) of Understanding VARs," American Economic Review, American Economic Association, vol. 97(3), pages 1021-1026, June.
    2. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2004. "The Response of Hours to a Technology Shock: Evidence Based on Direct Measures of Technology," Journal of the European Economic Association, MIT Press, vol. 2(2-3), pages 381-395, 04/05.
    3. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    4. Graham Elliott & Allan Timmermann, 2016. "Economic Forecasting," Economics Books, Princeton University Press, edition 1, number 10740.
    5. Coen N. Teulings & Nikolay Zubanov, 2014. "Is Economic Recovery A Myth? Robust Estimation Of Impulse Responses," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 497-514, April.
    6. Pesaran, M. Hashem & Pick, Andreas & Timmermann, Allan, 2011. "Variable selection, estimation and inference for multi-period forecasting problems," Journal of Econometrics, Elsevier, vol. 164(1), pages 173-187, September.
    7. Beveridge, Stephen & Nelson, Charles R., 1981. "A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the `business cycle'," Journal of Monetary Economics, Elsevier, vol. 7(2), pages 151-174.
    8. Òscar Jordà, 2005. "Estimation and Inference of Impulse Responses by Local Projections," American Economic Review, American Economic Association, vol. 95(1), pages 161-182, March.
    9. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
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    Citations

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    Cited by:

    1. Seitz, Franz & Baumann, Ursel & Albuquerque, Bruno, 2015. "The information content of money and credit for US activity," Working Paper Series 1803, European Central Bank.
    2. Albuquerque, Bruno & Baumann, Ursel & Seitz, Franz, 2016. "What does money and credit tell us about real activity in the United States?," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 328-347.
    3. repec:ecb:ecbwps:20141803 is not listed on IDEAS

    More about this item

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • E0 - Macroeconomics and Monetary Economics - - General

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