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Financial stress regimes and the macroeconomy

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  • Ana B. Galvão
  • Michael T. Owyang

Abstract

Some financial stress events lead to macroeconomic downturns, while others appear to be isolated to financial markets. We identify financial stress regimes using a model that explicitly links financial variables to macroeconomic outcomes. The stress regimes are identified using an unbalanced panel of financial variables with an embedded method for variable selection. Our identified stress regimes are associated with corporate credit tightening and with NBER recessions. An exogenous deterioration in our financial condition index has strong negative effects in economic activity, and negative amplification effects on inflation in the stress regime. We employ a novel factor-augmented vector autoregressive model with smooth regime changes (FAST-VAR).

Suggested Citation

  • Ana B. Galvão & Michael T. Owyang, 2014. "Financial stress regimes and the macroeconomy," Working Papers 2014-20, Federal Reserve Bank of St. Louis.
  • Handle: RePEc:fip:fedlwp:2014-020
    DOI: 10.20955/wp.2014.020
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    Cited by:

    1. Gian Paulo Soave, 2023. "A panel threshold VAR with stochastic volatility-in-mean model: an application to the effects of financial and uncertainty shocks in emerging economies," Applied Economics, Taylor & Francis Journals, vol. 55(4), pages 397-431, January.
    2. Granziera, Eleonora & Sekhposyan, Tatevik, 2019. "Predicting relative forecasting performance: An empirical investigation," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1636-1657.
    3. Vito Polito, 2020. "Nonlinear Business Cycle and Optimal Policy: A VSTAR Perspective," CESifo Working Paper Series 8060, CESifo.
    4. Cotter, John & Hallam, Mark & Yilmaz, Kamil, 2023. "Macro-financial spillovers," Journal of International Money and Finance, Elsevier, vol. 133(C).
    5. Mr. Luis Brandão-Marques & Mrs. Esther Perez Ruiz, 2017. "How Financial Conditions Matter Differently across Latin America," IMF Working Papers 2017/218, International Monetary Fund.
    6. Chiu, Ching-Wai (Jeremy) & Hacioglu Hoke, Sinem, 2016. "Macroeconomic tail events with non-linear Bayesian VARs," Bank of England working papers 611, Bank of England.
    7. Kocak, Emrah & Bilgili, Faik & Bulut, Umit & Kuskaya, Sevda, 2022. "Is ethanol production responsible for the increase in corn prices?," Renewable Energy, Elsevier, vol. 199(C), pages 689-696.
    8. Michael Dueker & Laura E Jackson & Michael T Owyang & Martin Sola, 2023. "A time-varying threshold STAR model with applications," Oxford Open Economics, Oxford University Press, vol. 2, pages 63-98.
    9. Sokbae Lee & Yuan Liao & Myung Hwan Seo & Youngki Shin, 2018. "Factor-Driven Two-Regime Regression," Papers 1810.11109, arXiv.org, revised Sep 2020.
    10. Nonejad, Nima, 2022. "Predicting equity premium out-of-sample by conditioning on newspaper-based uncertainty measures: A comparative study," International Review of Financial Analysis, Elsevier, vol. 83(C).
    11. Paolo Gorgi & Siem Jan Koopman & Julia Schaumburg, 2021. "Vector Autoregressions with Dynamic Factor Coefficients and Conditionally Heteroskedastic Errors," Tinbergen Institute Discussion Papers 21-056/III, Tinbergen Institute.

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    More about this item

    Keywords

    factor-augmented VAR models; Smooth Transition VAR models; Gibbs variable selection; financial crisis;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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