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Joint Bayesian inference about impulse responses in VAR models

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  • Inoue, Atsushi
  • Kilian, Lutz

Abstract

We derive the Bayes estimator of vectors of structural VAR impulse responses under a range of alternative loss functions. We also discuss the construction of joint credible regions for vectors of impulse responses as the lowest posterior risk region under the same loss functions. We show that conventional impulse response estimators such as the posterior median response function or the posterior mean response function are not in general the Bayes estimator of the impulse response vector obtained by stacking the impulse responses of interest. We illustrate that such pointwise estimators may imply response function shapes that are incompatible with any possible parameterization of the underlying model. Moreover, conventional pointwise quantile error bands are not a valid measure of the estimation uncertainty about the impulse response vector because they ignore the mutual dependence of the responses. In practice, they tend to understate substantially the estimation uncertainty about the impulse response vector.

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  • Inoue, Atsushi & Kilian, Lutz, 2022. "Joint Bayesian inference about impulse responses in VAR models," Journal of Econometrics, Elsevier, vol. 231(2), pages 457-476.
  • Handle: RePEc:eee:econom:v:231:y:2022:i:2:p:457-476
    DOI: 10.1016/j.jeconom.2021.05.010
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    Cited by:

    1. Finck, David & Tillmann, Peter, 2022. "The macroeconomic effects of global supply chain disruptions," BOFIT Discussion Papers 14/2022, Bank of Finland Institute for Emerging Economies (BOFIT).
    2. Atsushi Inoue & Lutz Kilian, 2020. "The Role of the Prior in Estimating VAR Models with Sign Restrictions," Working Papers 2030, Federal Reserve Bank of Dallas.
    3. Berger, Tino & Richter, Julia & Wong, Benjamin, 2022. "A unified approach for jointly estimating the business and financial cycle, and the role of financial factors," Journal of Economic Dynamics and Control, Elsevier, vol. 136(C).
    4. Kilian, Lutz & Nomikos, Nikos K. & Zhou, Xiaoqing, 2020. "A quantitative model of the oil tanker market in the Arabian Gulf," CFS Working Paper Series 648, Center for Financial Studies (CFS).
    5. Reichlin, Lucrezia & Ricco, Giovanni & Tarbé, Matthieu, 2023. "Monetary–fiscal crosswinds in the European Monetary Union," European Economic Review, Elsevier, vol. 151(C).
    6. Lutz Kilian & Xiaoqing Zhou, 2023. "Oil Price Shocks and Inflation," Working Papers 2312, Federal Reserve Bank of Dallas.
    7. Kilian, Lutz & Zhou, Xiaoqing, 2023. "A broader perspective on the inflationary effects of energy price shocks," Energy Economics, Elsevier, vol. 125(C).
    8. Kilian, Lutz & Zhou, Xiaoqing, 2022. "Oil prices, exchange rates and interest rates," Journal of International Money and Finance, Elsevier, vol. 126(C).
    9. Kilian, Lutz & Zhou, Xiaoqing, 2022. "The impact of rising oil prices on U.S. inflation and inflation expectations in 2020–23," Energy Economics, Elsevier, vol. 113(C).
    10. Diegel, Max & Nautz, Dieter, 2021. "Long-term inflation expectations and the transmission of monetary policy shocks: Evidence from a SVAR analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 130(C).
    11. Lutz Kilian, 2023. "How to Construct Monthly VAR Proxies Based on Daily Futures Market Surprises," Working Papers 2310, Federal Reserve Bank of Dallas.
    12. Raffaella Giacomini & Toru Kitagawa & Matthew Read, 2021. "Identification and Inference Under Narrative Restrictions," Papers 2102.06456, arXiv.org.
    13. Paul Carrillo‐Maldonado, 2023. "Partial identification for growth regimes: The case of Latin American countries," Metroeconomica, Wiley Blackwell, vol. 74(3), pages 557-583, July.
    14. Diab, Sara & Karaki, Mohamad B., 2023. "Do increases in gasoline prices cause higher food prices?," Energy Economics, Elsevier, vol. 127(PB).
    15. Finck, David & Tillmann, Peter, 2023. "The macroeconomic effects of global supply chain disruptions," IMFS Working Paper Series 178, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    16. Bruns, Martin, 2021. "Proxy Vector Autoregressions in a Data-rich Environment," Journal of Economic Dynamics and Control, Elsevier, vol. 123(C).

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    More about this item

    Keywords

    Loss function; Joint inference; Median response function; Mean response function; Modal model; Posterior risk;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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