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Canadian Monetary Policy Analysis using a Structural VARMA Model

Author

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  • Mala Raghavan
  • George Athanasopoulos
  • Param Silvapulle

Abstract

This paper builds a structural VARMA (SVARMA) model for investigating Canadian monetary policy. Despite the support for a VARMA model for monetary policy analysis, the traditional VAR and SVAR models have predominantly been used in the literature mainly due to difficulties associated with the identification and estimation of such a model. Using the scalar component model (SCM) proposed by Athanasopoulos and Vahid (2008a), this paper first identifies a VARMA model and then constructs a SVARMA model for Canadian monetary policy. We included the SVAR model in our study for a comparison purpose. Relative to this model, the impulse responses generated by the SVARMA model appear to be consistent with those predicted by various economic theoretical models, and solves the economic puzzles found commonly in the empirical literature on monetary policy. The successful construction and implementation of the SVARMA model for Canadian monetary policy analysis along with its promising impulse responses, indicate the suitability of this framework for small open economies.

Suggested Citation

  • Mala Raghavan & George Athanasopoulos & Param Silvapulle, 2013. "Canadian Monetary Policy Analysis using a Structural VARMA Model," Monash Econometrics and Business Statistics Working Papers 4/13, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2013-4
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    References listed on IDEAS

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    Cited by:

    1. Raghavan, Mala, 2020. "An analysis of the global oil market using SVARMA models," Energy Economics, Elsevier, vol. 86(C).
    2. 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.
    3. 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).
    4. 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.
    5. 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.
    6. Bernd Funovits, 2020. "Identifiability and Estimation of Possibly Non-Invertible SVARMA Models: A New Parametrisation," Papers 2002.04346, arXiv.org, revised Feb 2021.
    7. 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.
    8. 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.
    9. Bernd Funovits, 2019. "Identification and Estimation of SVARMA models with Independent and Non-Gaussian Inputs," Papers 1910.04087, arXiv.org.
    10. Funovits, Bernd, 2024. "Identifiability and estimation of possibly non-invertible SVARMA Models: The normalised canonical WHF parametrisation," Journal of Econometrics, Elsevier, vol. 241(2).

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    More about this item

    Keywords

    VARMA models; Identification; Impulse responses; Open economy; Transmission mechanism;
    All these keywords.

    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

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