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Structural Vector Autoregressions with Heteroskedasticity - A Comparison of Different Volatility Models

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  • Helmut Luetkepohl
  • Aleksei Netšunajev

Abstract

A growing literature uses changes in residual volatility for identifying structural shocks in vector autoregressive (VAR) analysis. A number of different models for heteroskedasticity or conditional heteroskedasticity are proposed and used in applications in this context. This study reviews the different volatility models and points out their advantages and drawbacks. It thereby enables researchers wishing to use identification of structural VAR models via heteroskedasticity to make a more informed choice of a suitable model for a specific empirical analysis. An application investigating the interaction between U.S. monetary policy and the stock market is used to illustrate the related issues.

Suggested Citation

  • Helmut Luetkepohl & Aleksei Netšunajev, 2015. "Structural Vector Autoregressions with Heteroskedasticity - A Comparison of Different Volatility Models," CESifo Working Paper Series 5308, CESifo Group Munich.
  • Handle: RePEc:ces:ceswps:_5308
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    References listed on IDEAS

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    More about this item

    Keywords

    structural vector autoregression; identification via heteroskedasticity; conditional heteroskedasticity; smooth transition; Markov switching; GARCH;

    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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