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Structural Vector Autoregressions with Nonnormal Residuals

Author

Listed:
  • Markku Lanne
  • Helmut Luetkepohl

Abstract

In structural vector autoregressive (SVAR) models identifying restrictions for shocks and impulse responses are usually derived from economic theory or institutional constraints. Sometimes the restrictions are insufficient for identifying all shocks and impulse responses. In this paper it is pointed out that specific distributional assumptions can also help in identifying the structural shocks. In particular, a mixture of normal distributions is considered as a plausible model that can be used in this context. Our model setup makes it possible to test restrictions which are just-identifying in a standard SVAR framework. In particular, we can test for the number of transitory and permanent shocks in a cointegrated SVAR model. The results are illustrated using a data set from King, Plosser, Stock and Watson (1991) and a system of US and European interest rates.Classification-JEL: C32

Suggested Citation

  • Markku Lanne & Helmut Luetkepohl, 2005. "Structural Vector Autoregressions with Nonnormal Residuals," Economics Working Papers ECO2005/25, European University Institute.
    • Handle: RePEc:eui:euiwps:eco2005/25
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    References listed on IDEAS

    as
    1. Nikolaus A. Siegfried, 2002. "An information-theoretic extension to structural VAR modelling," Quantitative Macroeconomics Working Papers 20203, Hamburg University, Department of Economics.
    2. Helmut Lütkepohl, 2005. "New Introduction to Multiple Time Series Analysis," Springer Books, Springer, number 978-3-540-27752-1, July.
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    4. Ralf Brüggemann & Helmut Lütkepohl, 2005. "Uncovered Interest Rate Parity and the Expectations Hypothesis of the Term Structure: Empirical Results for the U.S. and Europe," SFB 649 Discussion Papers SFB649DP2005-035, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    5. King, Robert G. & Plosser, Charles I. & Stock, James H. & Watson, Mark W., 1991. "Stochastic Trends and Economic Fluctuations," American Economic Review, American Economic Association, vol. 81(4), pages 819-840, September.
    6. Bénédicte Vidaillet & V. d'Estaintot & P. Abécassis, 2005. "Introduction," Post-Print hal-00287137, HAL.
    7. Evans, Charles L. & Marshall, David A., 1998. "Monetary policy and the term structure of nominal interest rates: Evidence and theory," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 49(1), pages 53-111, December.
    8. Brüggemann, Ralf & Lütkepohl, Helmut, 2005. "Uncovered interest rate parity and the expectations hypothesis of the term structure: Empirical results for the US and Europe," SFB 649 Discussion Papers 2005-035, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    9. Boswijk, H. Peter & Lucas, Andre, 2002. "Semi-nonparametric cointegration testing," Journal of Econometrics, Elsevier, vol. 108(2), pages 253-280, June.
    10. Benkwitz, Alexander & Lütkepohl, Helmut & Wolters, Jürgen, 2001. "Comparison Of Bootstrap Confidence Intervals For Impulse Responses Of German Monetary Systems," Macroeconomic Dynamics, Cambridge University Press, vol. 5(1), pages 81-100, February.
    11. Lanne, Markku & Saikkonen, Pentti, 2007. "A Multivariate Generalized Orthogonal Factor GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 61-75, January.
    12. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    13. Lucas, André, 1997. "Cointegration Testing Using Pseudolikelihood Ratio Tests," Econometric Theory, Cambridge University Press, vol. 13(2), pages 149-169, April.
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    More about this item

    Keywords

    Mixture normal distribution; cointegration; vector autoregressive process; vector error correction model; impulse responses;
    All these keywords.

    JEL classification:

    • 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

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