IDEAS home Printed from https://ideas.repec.org/a/cje/issued/v49y2016i1p347-373.html

Canadian monetary policy analysis using a structural VARMA model

Author

Listed:
  • Mala Raghavan
  • George Athanasopoulos
  • Param Silvapulle

Abstract

This paper builds a structural VARMA (SVARMA) model for investigating Canadian monetary policy. Using the scalar component methodology proposed by Athanasopoulos and Vahid (2008a), we first identify a VARMA model and then construct a SVARMA for Canadian monetary policy. Relative to the responses by a structural VAR, the responses generated by the SVARMA are consistent with those supported by various theoretical models and solve economic puzzles commonly found in the empirical literature on monetary policy. The superior out-of-sample forecasting performance of the reduced form VARMA compared to VAR alternatives further advocates the suitability of this framework for small open economies.

Suggested Citation

  • Mala Raghavan & George Athanasopoulos & Param Silvapulle, 2016. "Canadian monetary policy analysis using a structural VARMA model," Canadian Journal of Economics, Canadian Economics Association, vol. 49(1), pages 347-373, February.
    • Handle: RePEc:cje:issued:v:49:y:2016:i:1:p:347-373
    DOI: 10.1111/caje.12200
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/caje.12200
    Download Restriction: access restricted to subscribers
    File URL: https://libkey.io/10.1111/caje.12200?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or

    for a different version of it.

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Raghavan, Mala, 2020. "An analysis of the global oil market using SVARMA models," Energy Economics, Elsevier, vol. 86(C).
    2. Bernd Funovits, 2019. "Identification and Estimation of SVARMA models with Independent and Non-Gaussian Inputs," Papers 1910.04087, arXiv.org.
    3. Xu Xiaojie, 2018. "Using Local Information to Improve Short-Run Corn Price Forecasts," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 16(1), pages 1-15, January.
    4. Georgiadis, Georgios & Jančoková, Martina, 2020. "Financial globalisation, monetary policy spillovers and macro-modelling: Tales from 1001 shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 121(C).
    5. Guido Turnip, 2017. "Identification of Small Open Economy SVARs via Markov-Switching Heteroskedasticity," The Economic Record, The Economic Society of Australia, vol. 93(302), pages 465-483, September.
    6. Raghavan, Mala & Athanasopoulos, George, 2019. "Analysis of shock transmissions to a small open emerging economy using a SVARMA model," Economic Modelling, Elsevier, vol. 77(C), pages 187-203.
    7. Bernd Funovits, 2020. "Identifiability and Estimation of Possibly Non-Invertible SVARMA Models: A New Parametrisation," Papers 2002.04346, arXiv.org, revised Feb 2021.
    8. Funovits, Bernd, 2024. "Identifiability and estimation of possibly non-invertible SVARMA Models: The normalised canonical WHF parametrisation," Journal of Econometrics, Elsevier, vol. 241(2).
    9. Bhattacharya, Rudrani & Tripathi, Shruti & Chowdhury, Sahana Roy, 2019. "Financial structure, institutional quality and monetary policy transmission: A Meta-Analysis," Working Papers 19/274, National Institute of Public Finance and Policy.
    10. I. B. Petrosyan & Z. K. Papian, 2025. "Possibilities of Implementing Countercyclical and Procyclical Economic Policy in Times of Global Economic Crises the Case of the Republic of Armenia," Studies on Russian Economic Development, Springer, vol. 36(2), pages 175-184, April.

    More about this item

    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

    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:cje:issued:v:49:y:2016:i:1:p:347-373. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Prof. Werner Antweiler (email available below). General contact details of provider: https://edirc.repec.org/data/ceaaaea.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.