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Regime-switching global vector autoregressive models

Author

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  • Gross, Marco
  • Binder, Michael

Abstract

The purpose of the paper is to develop a Regime-Switching Global Vector Autoregressive (RS-GVAR) model. The RS-GVAR model allows for recurring or non-recurring structural changes in all or a subset of countries. It can be used to generate regime-dependent impulse response functions which are conditional upon a regime-constellation across countries. Coupling the RS and the GVAR methodology improves out-of-sample forecast accuracy significantly in an application to real GDP, price inflation, and stock prices. JEL Classification: C32, E17, G20

Suggested Citation

  • Gross, Marco & Binder, Michael, 2013. "Regime-switching global vector autoregressive models," Working Paper Series 1569, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20131569
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Kok, Christoffer & Gross, Marco & Żochowski, Dawid, 2016. "The impact of bank capital on economic activity - evidence from a mixed-cross-section GVAR model," Working Paper Series 1888, European Central Bank.
    2. Dovern, Jonas & Huber, Florian, 2015. "Global prediction of recessions," Economics Letters, Elsevier, vol. 133(C), pages 81-84.
    3. Gross, Marco & Población García, Francisco Javier, 2016. "Assessing the efficacy of borrower-based macroprudential policy using an integrated micro-macro model for European households," Working Paper Series 1881, European Central Bank.
    4. Olfa Manai Daboussi & Amira Majoul, 2014. "The transmission effects of the subprime crisis to emerging markets: A global VAR analysis," Asian Journal of Empirical Research, Asian Economic and Social Society, vol. 4(9), pages 427-438, September.
    5. Peltonen, Tuomas & Gross, Marco & Behn, Markus, 2016. "Assessing the costs and benefits of capital-based macroprudential policy," Working Paper Series 1935, European Central Bank.
    6. Gutierrez, Luciano & Piras, Francesco, 2014. "A global VAR model for the analysis of wheat export prices," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182723, European Association of Agricultural Economists.
    7. Muhsin Kar & Tayfur Bayat & Selim Kayhan, 2016. "Impacts of Credit Default Swaps on Volatility of the Exchange Rate in Turkey: The Case of Euro," International Journal of Financial Studies, MDPI, Open Access Journal, vol. 4(3), pages 1-18, July.
    8. Semmler, Willi & Gross, Marco, 2017. "Mind the output gap: the disconnect of growth and inflation during recessions and convex Phillips curves in the euro area," Working Paper Series 2004, European Central Bank.
    9. José Da Fonseca & Peiming Wang, 2016. "A joint analysis of market indexes in credit default swap, volatility and stock markets," Applied Economics, Taylor & Francis Journals, vol. 48(19), pages 1767-1784, April.

    More about this item

    Keywords

    forecasting and simulation; Global macroeconometric modeling; nonlinear modeling; regime switching;

    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
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

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