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Proxy VAR Models in a Data-Rich Environment

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  • Martin Bruns

Abstract

Structural VAR models require two ingredients: (i) Informational sufficiency, and (ii) a valid identification strategy. These conditions are unlikely to be met by small-scale recursively identified VAR models. I propose a Bayesian Proxy Factor-Augmented VAR (BP-FAVAR) to combine a large information set with an identification scheme based on an external instrument. In an application to monetary policy shocks I find that augmenting a standard small-scale Proxy VAR by factors from a large set of financial variables changes the model dynamics and delivers price responses which are more in line with economic theory. A second application shows that an exogenous increase in uncertainty affects disaggregated investment series more negatively than consumption series.

Suggested Citation

  • Martin Bruns, 2019. "Proxy VAR Models in a Data-Rich Environment," Discussion Papers of DIW Berlin 1831, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp1831
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    References listed on IDEAS

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    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • E60 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General

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