IDEAS home Printed from https://ideas.repec.org/a/qua/journl/v10y2013i2p135-141.html
   My bibliography  Save this article

Generalized impulse response analysis: General or Extreme?

Author

Listed:
  • Hyeongwoo Kim

    (Auburn University)

Abstract

This note discusses a pitfall of using the generalized impulse response function (GIRF) in vector autoregressive (VAR) models (Pesaran and Shin, 1998). The GIRF is general because it is invariant to the ordering of the variables in the VAR. The GIRF, in fact, is extreme because it yields a set of response functions that are based on extreme identifying assumptions that contradict each other, unless the covariance matrix is diagonal. With a help of empirical examples, the present note demonstrates that the GIRF may yield quite misleading economic inferences.

Suggested Citation

  • Hyeongwoo Kim, 2013. "Generalized impulse response analysis: General or Extreme?," EconoQuantum, Revista de Economia y Finanzas, Universidad de Guadalajara, Centro Universitario de Ciencias Economico Administrativas, Departamento de Metodos Cuantitativos y Maestria en Economia., vol. 10(2), pages 135-141, Julio – D.
    • Handle: RePEc:qua:journl:v:10:y:2013:i:2:p:135-141
    as

    Download full text from publisher

    File URL: http://econoquantum.cucea.udg.mx/index.php/EQ/article/view/165
    Download Restriction: no
    File URL: http://econoquantum.cucea.udg.mx/index.php/EQ/issue/view/24
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Cheung, Yin-Wong & Lai, Kon S. & Bergman, Michael, 2004. "Dissecting the PPP puzzle: the unconventional roles of nominal exchange rate and price adjustments," Journal of International Economics, Elsevier, vol. 64(1), pages 135-150, October.
    2. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
    3. Boyd, Derick & Caporale, Gugielmo Maria & Smith, Ron, 2001. "Real Exchange Rate Effects on the Balance of Trade: Cointegration and the Marshall-Lerner Condition," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 6(3), pages 187-200, July.
    4. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    5. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    6. Huang, Ying & Neftci, Salih N. & Guo, Feng, 2008. "Swap curve dynamics across markets: Case of US dollar versus HK dollar," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(1), pages 79-93, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ratti, Ronald A. & Vespignani, Joaquin L., 2015. "Commodity prices and BRIC and G3 liquidity: A SFAVEC approach," Journal of Banking & Finance, Elsevier, vol. 53(C), pages 18-33.
    2. Younes Boujelbène & Majdi Ksantini, 2009. "La transmission entre les marchés boursiers :Une analyse en composante principale," Brussels Economic Review, ULB -- Universite Libre de Bruxelles, vol. 52(2), pages 161-194.
    3. Victor Echevarria Icaza & Simón Sosvilla-Rivero, 2017. "Yields on sovereign debt, fragmentation and monetary policy transmission in the euro area: A GVAR approach," Working Papers 17-01, Asociación Española de Economía y Finanzas Internacionales.
    4. Pesaran, M. Hashem & Schuermann, Til & Treutler, Bjorn-Jakob & Weiner, Scott M., 2006. "Macroeconomic Dynamics and Credit Risk: A Global Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1211-1261, August.
    5. Ewing, Bradley T. & Sari, Ramazan & Soytas, Ugur, 2007. "Disaggregate energy consumption and industrial output in the United States," Energy Policy, Elsevier, vol. 35(2), pages 1274-1281, February.
    6. Han, Lin & Kordzakhia, Nino & Trück, Stefan, 2020. "Volatility spillovers in Australian electricity markets," Energy Economics, Elsevier, vol. 90(C).
    7. Kenneth F. Wallis & Jan P. A. M. Jacobs, 2005. "Comparing SVARs and SEMs: two models of the UK economy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(2), pages 209-228.
    8. Claus Brand & Nuno Cassola, 2004. "A money demand system for euro area M3," Applied Economics, Taylor & Francis Journals, vol. 36(8), pages 817-838.
    9. François-Éric Racicot & Raymond Théoret, 2022. "Tracking market and non-traditional sources of risks in procyclical and countercyclical hedge fund strategies under extreme scenarios: a nonlinear VAR approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-56, December.
    10. Antonakakis, Nikolaos & Cunado, Juncal & Filis, George & Gabauer, David & Perez de Gracia, Fernando, 2018. "Oil volatility, oil and gas firms and portfolio diversification," Energy Economics, Elsevier, vol. 70(C), pages 499-515.
    11. Dees, Stéphane, 2016. "Credit, asset prices and business cycles at the global level," Economic Modelling, Elsevier, vol. 54(C), pages 139-152.
    12. Apergis, Nicholas & Baruník, Jozef & Lau, Marco Chi Keung, 2017. "Good volatility, bad volatility: What drives the asymmetric connectedness of Australian electricity markets?," Energy Economics, Elsevier, vol. 66(C), pages 108-115.
    13. Adriana AnaMaria Davidescu & Eduard Mihai Manta & Razvan Gabriel Hapau & Mihaela Gruiescu & Oana Mihaela Vacaru (Boita), 2023. "Exploring the Contagion Effect from Developed to Emerging CEE Financial Markets," Mathematics, MDPI, vol. 11(3), pages 1-50, January.
    14. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Filis, George, 2017. "Oil shocks and stock markets: Dynamic connectedness under the prism of recent geopolitical and economic unrest," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 1-26.
    15. Erdal Demirhan & Banu Demirhan, 2015. "The Dynamic Effect of ExchangeRate Volatility on Turkish Exports: Parsimonious Error-Correction Model Approach," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 62(4), pages 429-451, September.
    16. Diego Silveira Pacheco de Oliveira & Gabriel Caldas Montes, 2020. "Sovereign credit news and disagreement in expectations about the exchange rate: evidence from Brazil," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 48(3), pages 660-698, August.
    17. Pesaran M.H. & Schuermann T. & Weiner S.M., 2004. "Modeling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 129-162, April.
    18. Helder Mendonça & Felipe Tostes, 2015. "The Effect of Monetary and Fiscal Credibility on Exchange Rate Pass-Through in an Emerging Economy," Open Economies Review, Springer, vol. 26(4), pages 787-816, September.
    19. Gabriel Caldas Montes & Victor Maia, 2023. "The reaction of disagreements in inflation expectations to fiscal sentiment obtained from information in official communiqués," Bulletin of Economic Research, Wiley Blackwell, vol. 75(4), pages 828-859, October.
    20. Chatziantoniou, Ioannis & Gabauer, David & Stenfors, Alexis, 2020. "From CIP-deviations to a market for risk premia: A dynamic investigation of cross-currency basis swaps," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 69(C).

    More about this item

    Keywords

    Generalized Impulse Response Function; Orthogonalized Impulse Response Function; Vector Autoregressive Models.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:qua:journl:v:10:y:2013:i:2:p:135-141. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sandra Ivett Portugal Padilla (email available below). General contact details of provider: https://edirc.repec.org/data/dmudgmx.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.