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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. Analyse de la politique monétaire du Canada à l'aide d'un modèle structurel VARMA. Ce texte construit un modèle structurel VARMA (SVARMA) pour étudier la politique monétaire du Canada. Utilisant la méthodologie de composante scalaire proposée par Athanasopoulos et Vahid (2008a), on identifie un modèle VARMA et on construit alors un modèle SVARMA pour la politique monétaire canadienne. Par rapport aux réponses par un modèle sructurel VAR, les réponses générées par SVARMA sont consistantes avec celles supportées par les divers modèles théoriques et résolvent les puzzles économiques qu'on trouve habituellement dan la littérature empirique sur la politique monétaire. La performance supérieure de prévision hors de l'échantillon de la forme réduite VARMA par rapport à VAR indique la pertinence de ce cadre conceptuel pour les petites économies ouvertes.

Suggested Citation

  • Mala Raghavan & George Athanasopoulos & Param Silvapulle, 2016. "Canadian monetary policy analysis using a structural VARMA model," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 49(1), pages 347-373, February.
    • Handle: RePEc:wly:canjec:v:49:y:2016:i:1:p:347-373
    DOI: 10.1111/caje.12200
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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. Bernd Funovits, 2019. "Identification and Estimation of SVARMA models with Independent and Non-Gaussian Inputs," Papers 1910.04087, arXiv.org.
    9. 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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    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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