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A Large Bayesian VAR of the U.S. Economy

Author

Listed:
  • Richard K. Crump

    (Federal Reserve Bank of New York)

  • Stefano Eusepi

    (Brown University)

  • Domenico Giannone

    (International Monetary Fund and University of Washington)

  • Eric Qian

    (Princeton University)

  • Argia Sbordone

    (Federal Reserve Bank of New York)

Abstract

We model the U.S. macroeconomic and financial sectors using a formal and unified econometric model. Through shrinkage, our Bayesian VAR provides a flexible framework for modeling the dynamics of 31 variables, many of which are tracked by the Federal Reserve. We show how the model can be used for understanding key features of the data, constructing counterfactual scenarios, and evaluating the macroeconomic environment both retrospectively and prospectively. Considering its breadth and versatility for policy applications, our modeling approach gives a reliable, reduced-form alternative to structural models.

Suggested Citation

  • Richard K. Crump & Stefano Eusepi & Domenico Giannone & Eric Qian & Argia Sbordone, 2025. "A Large Bayesian VAR of the U.S. Economy," International Journal of Central Banking, International Journal of Central Banking, vol. 21(2), pages 351-409, April.
    • Handle: RePEc:ijc:ijcjou:y:2025:q:2:a:8
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    References listed on IDEAS

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    1. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?," Journal of Econometrics, Elsevier, vol. 146(2), pages 318-328, October.
    2. Fabian Krüger & Todd E. Clark & Francesco Ravazzolo, 2017. "Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 470-485, July.
    3. Craig S. Hakkio & William R. Keeton, 2009. "Financial stress: what is it, how can it be measured, and why does it matter?," Economic Review, Federal Reserve Bank of Kansas City, vol. 94(Q II), pages 5-50.
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