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Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey

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  • Helmut Lütkepohl

Abstract

Large panels of variables are used by policy makers in deciding on policy actions. Therefore it is desirable to include large information sets in models for economic analysis. In this survey methods are reviewed for accounting for the information in large sets of variables in vector autoregressive (VAR) models. This can be done by aggregating the variables or by reducing the parameter space to a manageable dimension. Factor models reduce the space of variables whereas large Bayesian VAR models and panel VARs reduce the parameter space. Global VARs use a mixed approach. They aggregate the variables and use a parsimonious parametrisation. All these methods are discussed in this survey although the main emphasize is on factor models.

Suggested Citation

  • Helmut Lütkepohl, 2014. "Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey," Discussion Papers of DIW Berlin 1351, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp1351
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    Cited by:

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    3. Lucchetti, Riccardo & Venetis, Ioannis A., 2020. "A replication of "A quasi-maximum likelihood approach for large, approximate dynamic factor models" (Review of Economics and Statistics, 2012)," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 14, pages 1-14.
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    7. Nicholson, William B. & Matteson, David S. & Bien, Jacob, 2017. "VARX-L: Structured regularization for large vector autoregressions with exogenous variables," International Journal of Forecasting, Elsevier, vol. 33(3), pages 627-651.
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    Keywords

    factor models; structural vector autoregressive model; global vector autoregression; panel data; Bayesian vector autoregression;
    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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