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Multivariate Quantile Impulse Response Functions

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  • Gabriel Montes‐Rojas

Abstract

A reduced form multivariate quantile autoregressive model is developed to study heterogeneity in the effects of macroeconomic shocks. This framework is used for forecasting and for constructing quantile impulse response functions that explore dynamic heterogeneity in the response of endogenous variables to different shocks. The methodology allows evaluating different quantile paths, defined as the dynamic effects for a fix collection of quantile indexes. The model is applied to study monetary shocks in a three‐variable macroeconomic model (output gap, inflation, Fed Funds rate) for the USA for the period 1980q1–2010q1.

Suggested Citation

  • Gabriel Montes‐Rojas, 2019. "Multivariate Quantile Impulse Response Functions," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(5), pages 739-752, September.
    • Handle: RePEc:bla:jtsera:v:40:y:2019:i:5:p:739-752
    DOI: 10.1111/jtsa.12452
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    Cited by:

    1. Mehmet Balcilar & Zeynel Abidin Ozdemir & Huseyin Ozdemir & Gurcan Aygun & Mark E. Wohar, 2022. "Effectiveness of monetary policy under the high and low economic uncertainty states: evidence from the major Asian economies," Empirical Economics, Springer, vol. 63(4), pages 1741-1769, October.
    2. Dagli , Suzette & Mariano, Paul & Salvanera, Arjan Paulo, 2022. "Quantile Debt Fan Charts," ADB Economics Working Paper Series 664, Asian Development Bank.
    3. Balcilar, Mehmet & Ozdemir, Zeynel Abidin & Ozdemir, Huseyin & Wohar, Mark E., 2020. "Transmission of US and EU Economic Policy Uncertainty Shock to Asian Economies in Bad and Good Times," IZA Discussion Papers 13274, Institute of Labor Economics (IZA).
    4. Lee, Dong Jin & Kim, Tae-Hwan & Mizen, Paul, 2021. "Impulse response analysis in conditional quantile models with an application to monetary policy," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    5. Balcilar, Mehmet & Ozdemir, Zeynel Abidin & Ozdemir, Huseyin & Aygun, Gurcan & Wohar, Mark E., 2021. "Effectives of Monetary Policy under the High and Low Economic Uncertainty States: Evidence from the Major Asian Economies," IZA Discussion Papers 14420, Institute of Labor Economics (IZA).
    6. Yunyun Wang & Tatsushi Oka & Dan Zhu, 2023. "Distributional Vector Autoregression: Eliciting Macro and Financial Dependence," Papers 2303.04994, arXiv.org.
    7. Esra Alp Coşkun & Hakan Kahyaoglu & Chi Keung Marco Lau, 2023. "Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-34, December.
    8. Yuan, Di & Li, Sufang & Li, Rong & Zhang, Feipeng, 2022. "Economic policy uncertainty, oil and stock markets in BRIC: Evidence from quantiles analysis," Energy Economics, Elsevier, vol. 110(C).
    9. Marian Vavra, 2023. "Bias-Correction in Time Series Quantile Regression Models," Working and Discussion Papers WP 3/2023, Research Department, National Bank of Slovakia.
    10. Iacopini, Matteo & Poon, Aubrey & Rossini, Luca & Zhu, Dan, 2023. "Bayesian mixed-frequency quantile vector autoregression: Eliciting tail risks of monthly US GDP," Journal of Economic Dynamics and Control, Elsevier, vol. 157(C).
    11. Cho, Dooyeon & Han, Heejoon, 2021. "The tail behavior of safe haven currencies: A cross-quantilogram analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 70(C).
    12. Beutel, Johannes & Emter, Lorenz & Metiu, Norbert & Prieto, Esteban & Schüler, Yves, 2022. "The global financial cycle and macroeconomic tail risks," Discussion Papers 43/2022, Deutsche Bundesbank.

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