UNDERSTANDING CONSUMER PRICE INDEX DYNAMICS IN CANADA

  • Thabani NYONI University of Zimbabwe, Zimbabwe

Abstract

This research uses annual time series data on Consumer Price Index (CPI) in Canada from 1960 to 2017, to model and forecast CPI using the Box – Jenkins ARIMA technique. Diagnostic tests indicate that the C series is I (1). The study presents the ARIMA (1, 1, 1) model for predicting CPI in Canada. The diagnostic tests further show that the presented parsimonious model is stable. The results of the study apparently show that CPI in Canada is likely to continue on a sharp upwards trajectory in the next decade. The study encourages policy makers to make use of tight monetary and fiscal policy measures in order to control inflation in Canada.

References

[1] Box, G.E.P., and Jenkins, G.M. 1976. Time series analysis: forecasting and control, Holden – Day, ISBN: 0816211043, 9780816211043, 575 p.
[2] Brocwell, P.J., and Davis, R.A. 2002. Introduction to time series and forecasting, Fourth Edition, Springer-Verlag New York, ISBN: 978-0-387-21657-7, 437 p.
[3] Chatfield, C. 2003. The analysis of time series: An introduction, Chapman and Hall Publishing, ISBN: 978-1584883173, 352 p.
[4] Cryer, J.D., and Chan, K.S. 2008. Time series analysis with application in R, first edition, Springer-Verlag New York Publishing, ISBN: 978-0-387-75959-3, 491 p.
[5] Dhamo, E.G., Puka, L., and Zacaj, L. 2018. Forecasting Consumer Price Index (CPI) using time series models and multi-regression models (Albania Case Study), 10th International Scientific Conference “Business and Management 2018” May 3–4, 2018, Vilnius, LITHUANIA, ISBN 978-609-476-119-5
[6] Du, Y., et al. 2014. A novel divide-and-conquer model for CPI prediction using ARIMA, Gray Model and BPNN, Procedia Computer Science, 31 (2): 842-851, DOI: http://doi.org/10.1016/j.procs.2014.05.335
[7] Enke, D., and Mehdiyev, N. 2014. A hybrid Neuro-Fuzzy model to forecast inflation, Procedia Computer Science, 36 (2): 254-260, DOI: http://doi.org/10.1016/j.procs.2014.09.088
[8] Hurtado, C., et al. 2013. Forecasting Mexican inflation using neural networks, CONIELECOMP 2013, 23rd International Conference on Electronics, Communications and Computing, 11-13 March 2013, IEEE, Cholula, Mexico, DOI: http://doi.org/10.1109/CONIELECOMP.2013.6525753
[9] Kharimah, F., et al. 2015. Time series modelling and forecasting of the consumer price Bandar Lampung, Sci. Int (Lahore), 27 (5): 4119 – 4624.
[10] Manga, G.S. 1997. Mathematics and statistics for economics, Vikas Publishing House
[11] Nyoni, T., and Nathaniel, S.P. 2019. Modeling rates of inflation in Nigeria: An application of ARMA, ARIMA and GARCH models, Munich University Library – Munich Personal RePEc Archive (MPRA), Paper No. 91351.
[12] Nyoni, T. 2018. Modeling and forecasting inflation in Zimbabwe: A Generalized Autoregressive Conditionally Heteroskedastic (GARCH) approach, Munich University Library – Munich Personal RePEc Archive (MPRA), Paper No. 88132.
[13] Nyoni, T. 2018. Modeling Forecasting Naira / USD exchange rate in Nigeria: A Box – Jenkins ARIMA approach, University of Munich Library – Munich Personal RePEc Archive (MPRA), Paper No. 88622.
[14] Nyoni, T. 2018. Modeling and forecasting inflation in Kenya: Recent insights from ARIMA and GARCH analysis, Dimorian Review, 5 (6). ISSN: 2394-9163
[15] Nyoni, T. 2018. Box – Jenkins ARIMA approach to predicting net FDI inflows in Zimbabwe, Munich University Library – Munich Personal RePEc Archive (MPRA), Paper No. 87737.
[16] Sarangi, P.K., et al. 2018. Forecasting consumer price index using neural networks models, Innovative Practices in Operations Management and Information Technology – Apeejay School of Management
[17] Subhani, M.I., and Panjwani, K. 2009. Relationship between Consumer Price Index (CPI) and government bonds. South Asian Journal of Management Sciences, 3 (1): 11 – 17.
[18] Wei, W.S. 2006. Time series analysis: Univariate and multivariate methods, Second Edition, Pearson Education, ISBN: 0-321-32216-9, 605 p. Inc.
[19] Zivko, I., and Bosnjak, M. 2017. Time series modelling of inflation and its volatility in Croatia, Notitia Journal of Sustainable Development 3, Izvorni znanstveni rad/Original scientific paper UDC/UDK 336.748.12 (497.5)
Published
2019-06-30
How to Cite
NYONI, Thabani. UNDERSTANDING CONSUMER PRICE INDEX DYNAMICS IN CANADA. Theoretical and Practical Research in Economic Fields, [S.l.], v. 10, n. 1, p. 54-59, june 2019. ISSN 2068-7710. Available at: <https://journals.aserspublishing.eu/tpref/article/view/3815>. Date accessed: 19 apr. 2024. doi: https://doi.org/10.14505/tpref.v10.1(19).06.