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Impact factors

Author

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  • Omtzigt Pieter

    () (Faculty of Economics and Econometrics, WB Amsterdam)

  • Paruolo Paolo

    () (Department of Economics, University of Insubria, Italy)

Abstract

In this paper we discuss sensitivity of forecast with respect to the information set considered in prediction; we define a sensitivit measure called impact factor, IF. We calculate this measure in VAR processes integrated of order 0, 1 and 2. For VAR processes this measure is as simple function of the impulse response coefficients. For integrated VAR systems this measure is shown to have a direct interpretation in terms of long-run forecasts. Various applications of this concept are reviewed, including one on the interpretation and effectiveness of economics policies and one on the sensitivity of forecasts with respect to data revisions. A unified approach to inference on the IF is given, showing under what circumstances standard asymptotic inference can be conducted also in systems integrated of order 1 and 2.

Suggested Citation

  • Omtzigt Pieter & Paruolo Paolo, 2002. "Impact factors," Economics and Quantitative Methods qf0203, Department of Economics, University of Insubria.
  • Handle: RePEc:ins:quaeco:qf0203
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    References listed on IDEAS

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    Cited by:

    1. Paruolo, Paolo, 2006. "Common trends and cycles in I(2) VAR systems," Journal of Econometrics, Elsevier, vol. 132(1), pages 143-168, May.
    2. Paruolo Paolo, 2002. "Testing for common trends in conditional I(2) VAR models," Economics and Quantitative Methods qf0216, Department of Economics, University of Insubria.
    3. Girardi, Riccardo & Paruolo, Paolo, 2013. "Wages and prices in Europe before and after the onset of the Monetary Union," Economic Modelling, Elsevier, vol. 35(C), pages 643-653.
    4. Paolo Paruolo & Ben Murphy & Greet Janssens-Maenhout, 2011. "Do emissions and income have a common trend? A country-specific, time-series, global analysis, 1970-2008," Economics and Quantitative Methods qf1113, Department of Economics, University of Insubria.
    5. Paolo Paruolo, 2006. "The Likelihood Ratio Test for the Rank of a Cointegration Submatrix," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 68(s1), pages 921-948, December.
    6. Fanelli, Luca & Paruolo, Paolo, 2010. "Speed of adjustment in cointegrated systems," Journal of Econometrics, Elsevier, vol. 158(1), pages 130-141, September.
    7. Massimo Franchi & Paolo Paruolo, 2017. "A general inversion theorem for cointegration," DSS Empirical Economics and Econometrics Working Papers Series 2017/3, Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome.
    8. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2014. "Dynamic Factor Models, Cointegration and Error Correction Mechanisms," Working Papers ECARES ECARES 2014-14, ULB -- Universite Libre de Bruxelles.

    More about this item

    Keywords

    Forecasting; cointegration; dynamic multipliers; (Generalized) impulse responses; VAR.;

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