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Structural vector autoregressive analysis for cointegrated variables

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  • Helmut Lütkepohl

Abstract

Vector autoregressive (VAR) models are capable of capturing the dynamic structure of many time series variables. Impulse response functions are typically used to investigate the relationships between the variables included in such models. In this context the relevant impulses or innovations or shocks to be traced out in an impulse response analysis have to be specified by imposing appropriate identifying restrictions. Taking into account the cointegration structure of the variables offers interesting possibilities for imposing identifying restrictions. Therefore VAR models which explicitly take into account the cointegration structure of the variables, so-called vector error correction models, are considered. Specification, estimation and validation of reduced form vector error correction models is briefly outlined and imposing structural short- and long-run restrictions within these models is discussed.
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Suggested Citation

  • Helmut Lütkepohl, 2006. "Structural vector autoregressive analysis for cointegrated variables," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 75-88, March.
  • Handle: RePEc:spr:alstar:v:90:y:2006:i:1:p:75-88
    DOI: 10.1007/s10182-006-0222-4
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    References listed on IDEAS

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    1. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871, May.
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    8. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
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    More about this item

    Keywords

    Cointegration; vector autoregressive process; vector error correction model JEL C32;
    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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