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Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models

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  • Markku Lanne

    () (University of Helsinki and CREATES)

  • Henri Nyberg

    () (University of Helsinki)

Abstract

We propose a new generalized forecast error variance decomposition with the property that the proportions of the impact accounted for by innovations in each variable sum to unity. Our decomposition is based on the well-established concept of the generalized impulse response function. The use of the new decomposition is illustrated with an empirical application to U.S. output growth and interest rate spread data.

Suggested Citation

  • Markku Lanne & Henri Nyberg, 2014. "Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models," CREATES Research Papers 2014-17, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2014-17
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    References listed on IDEAS

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    5. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    6. Weise, Charles L, 1999. "The Asymmetric Effects of Monetary Policy: A Nonlinear Vector Autoregression Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 31(1), pages 85-108, February.
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    8. Galbraith, John W. & Tkacz, Greg, 2000. "Testing for asymmetry in the link between the yield spread and output in the G-7 countries," Journal of International Money and Finance, Elsevier, vol. 19(5), pages 657-672, October.
    9. Gilchrist, Simon & Yankov, Vladimir & Zakrajsek, Egon, 2009. "Credit market shocks and economic fluctuations: Evidence from corporate bond and stock markets," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 471-493, May.
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    12. Giovanni Caggiano & Efrem Castelnuovo & Valentina Colombo & Gabriela Nodari, 2015. "Estimating Fiscal Multipliers: News From A Non‐linear World," Economic Journal, Royal Economic Society, vol. 0(584), pages 746-776, May.
    13. Nathan S. Balke, 2000. "Credit and Economic Activity: Credit Regimes and Nonlinear Propagation of Shocks," The Review of Economics and Statistics, MIT Press, vol. 82(2), pages 344-349, May.
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    Cited by:

    1. Giovanni Caggiano & Efrem Castelnuovo & Juan Manuel Figueres, 2017. "Economic Policy Uncertainty Spillovers in Booms and Busts," Melbourne Institute Working Paper Series wp2017n13, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    2. Luca Barbaglia & Christophe Croux & Ines Wilms, 2017. "Volatility Spillovers and Heavy Tails: A Large t-Vector AutoRegressive Approach," Papers 1708.02073, arXiv.org.
    3. repec:eee:ecmode:v:76:y:2019:i:c:p:101-116 is not listed on IDEAS
    4. Caggiano, Giovanni & Castelnuovo, Efrem & Figueres, Juan Manuel, 2017. "Economic policy uncertainty and unemployment in the United States: A nonlinear approach," Economics Letters, Elsevier, vol. 151(C), pages 31-34.
    5. Raul Ibarra, 2016. "How important is the credit channel in the transmission of monetary policy in Mexico?," Applied Economics, Taylor & Francis Journals, vol. 48(36), pages 3462-3484, August.
    6. Caggiano, Giovanni & Castelnuovo, Efrem & Pellegrino, Giovanni, 2017. "Estimating the real effects of uncertainty shocks at the Zero Lower Bound," European Economic Review, Elsevier, vol. 100(C), pages 257-272.
    7. David Ubilava, 2018. "The Role of El Niño Southern Oscillation in Commodity Price Movement and Predictability," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 100(1), pages 239-263.
    8. SBIA, Rashid & Al Rousan, Sahel, 2015. "Does Financial Development Induce Economic Growth in UAE? The Role of Foreign Direct Investment and Capitalization," MPRA Paper 64599, University Library of Munich, Germany.
    9. Nyholm, Ken, 2016. "US-euro area term structure spillovers, implications for central banks," Working Paper Series 1980, European Central Bank.
    10. Thiem, Christopher, 2018. "Cross-category, trans-Pacific spillovers of policy uncertainty and financial market volatility," Ruhr Economic Papers 782, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    11. Markku Lanne & Henri Nyberg, 2015. "Nonlinear dynamic interrelationships between real activity and stock returns," CREATES Research Papers 2015-36, Department of Economics and Business Economics, Aarhus University.
    12. repec:eco:journ1:2017-05-20 is not listed on IDEAS
    13. Donal Smith, 2016. "The International Impact of Financial Shocks: A Global VAR and Connectedness Measures Approach," Discussion Papers 16/07, Department of Economics, University of York.
    14. Lorenzo Bretscher & Alex Hsu & Andrea Tamoni, 2017. "Level and Volatility Shocks to Fiscal Policy: Term Structure Implications," 2017 Meeting Papers 258, Society for Economic Dynamics.
    15. Ahmed, Khalid & Rehman, Mujeeb Ur & Ozturk, Ilhan, 2017. "What drives carbon dioxide emissions in the long-run? Evidence from selected South Asian Countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1142-1153.
    16. Jorge A Chan-Lau, 2017. "Variance Decomposition Networks; Potential Pitfalls and a Simple Solution," IMF Working Papers 17/107, International Monetary Fund.
    17. Karamé, Frédéric, 2015. "Asymmetries and Markov-switching structural VAR," Journal of Economic Dynamics and Control, Elsevier, vol. 53(C), pages 85-102.

    More about this item

    Keywords

    Forecast error variance decomposition; generalized impulse response function; output growth; term spread;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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