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Structural Vector Autoregressions: Checking Identifying Long-run Restrictions via Heteroskedasticity

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  • Helmut Luetkepohl
  • Anton Velinov

Abstract

Long-run restrictions have been used extensively for identifying structural shocks in vector autoregressive (VAR) analysis. Such restrictions are typically just-identifying but can be checked by utilizing changes in volatility. This paper reviews and contrasts the volatility models that have been used for this purpose. Three main approaches have been used, exogenously generated changes in the unconditional residual covariance matrix, changing volatility modelled by a Markov switching mechanism and multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models. Using changes in volatility for checking long-run identifying restrictions in structural VAR analysis is illustrated by reconsidering models for identifying fundamental components of stock prices.

Suggested Citation

  • Helmut Luetkepohl & Anton Velinov, 2014. "Structural Vector Autoregressions: Checking Identifying Long-run Restrictions via Heteroskedasticity," CESifo Working Paper Series 4651, CESifo Group Munich.
  • Handle: RePEc:ces:ceswps:_4651
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Lütkepohl, Helmut & Netšunajev, Aleksei, 2017. "Structural vector autoregressions with heteroskedasticity: A review of different volatility models," Econometrics and Statistics, Elsevier, vol. 1(C), pages 2-18.
    2. repec:kap:iaecre:v:23:y:2017:i:2:d:10.1007_s11294-017-9635-y is not listed on IDEAS
    3. Helmut Lütkepohl & Thore Schlaak, 2018. "Choosing Between Different Time‐Varying Volatility Models for Structural Vector Autoregressive Analysis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(4), pages 715-735, August.
    4. repec:ris:apltrx:0340 is not listed on IDEAS
    5. Helmut Lütkepohl & Aleksei Netšunajev, 2018. "The Relation between Monetary Policy and the Stock Market in Europe," Econometrics, MDPI, Open Access Journal, vol. 6(3), pages 1-14, August.
    6. Velinov, Anton & Chen, Wenjuan, 2015. "Do stock prices reflect their fundamentals? New evidence in the aftermath of the financial crisis," Journal of Economics and Business, Elsevier, vol. 80(C), pages 1-20.
    7. Puonti, Päivi, 2016. "Fiscal multipliers in a structural VEC model with mixed normal errors," Journal of Macroeconomics, Elsevier, vol. 48(C), pages 144-154.
    8. Anton Velinov & Wenjuan Chen, 2014. "Are There Bubbles in Stock Prices?: Testing for Fundamental Shocks," Discussion Papers of DIW Berlin 1375, DIW Berlin, German Institute for Economic Research.
    9. Helmut Lütkepohl & Tomasz Woźniak, 2017. "Bayesian Inference for Structural Vector Autoregressions Identified by Markov-Switching Heteroskedasticity," Discussion Papers of DIW Berlin 1707, DIW Berlin, German Institute for Economic Research.

    More about this item

    Keywords

    vector autoregression; heteroskedasticity; vector GARCH; conditional heteroskedasticity; Markov switching model;

    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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