AN AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE MODEL OF INFLATION IN MOLDOVA WITH SOME OBSERVATIONS ON THE INFLATION OUTLOOK
Keywords:Auto-Regressive Integrated Moving Average models, Inflation, Moldova
The paper discusses the properties of Auto-Regressive Integrated Moving Average (ARIMA) models and proceeds to estimate a model for the monthly evolution of the annual inflation rate in Moldova from January 2013 to October 2021. The aim of the paper is to develop a model relying exclusively upon the historical evolution of inflation as an additional instrument for forecasting purposes. The estimated model explains close to 97 % of the monthly variation of the inflation rate over the model’s estimation period and is used to generate forecasts of the monthly evolution of the annual inflation rate in short to medium term. The ARIMA-generated forecasts suggest that the acceleration of inflation which characterised the monthly evolution of the annual inflation rate in 2021 up to October 2021 will continue in the next four months, with the inflation rate peaking at 12 % in February 2022 and slowly decelerating from that point onwards towards the 5 % inflation target in the longer term. The paper concludes by suggesting areas for further work and briefly discussing the inflation outlook for the Moldovan economy, considering current international and domestic economic conditions. Natural areas for further work would be to regularly update the econometric estimates and forecasts of the estimated ARIMA model as the economy evolves through time. With regard to the inflation outlook, the analysis contained in the concluding section of the paper suggests that the future evolution of inflation is likely to be more pessimistic than the ARIMA-based generated forecast.
Box, G., & Jenkins, G. (1970). Time Series Analysis: Forecasting and control, San Francisco: Holden-Day.
Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th Edition). New Jersey: Published by John Wiley and Sons Inc.
Kelikume, I. & Salami, A. (2014). Time Series Modeling and Forecasting Inflation: Evidence from Nigeria. The International Journal of Business and Finance Research, 8(2), 41-51. https://ssrn.com/abstract=2322918
Meyler, A., Kenny, G. & Quin, T. (1998, December). Forecasting Irish Inflation using ARIMA Models. Technical Paper, 3/RT, 46. Economic Analysis, Research and Publications Department, Central Bank of Ireland. Dublin. https://www.centralbank.ie/docs/default-source/publications/research-technical-papers/3rt98---forecasting-irish-inflation-using-arima-models-(kenny-meyler-and-quinn).pdf
Mija, S., Slobozian, D., Cuhal, R., & Stratan, A. (2013, March). How Core Inflation Reacts to the Second Round Effects. Institute for Economic Forecasting. Journal for Economic Forecasting, 1, 98-118.
National Bank of Moldova. (2021, November). Inflation Report No. 4. https://www.bnm.md/en/content/inflation-report-no-4-2021-has-been-published
Pintilescu, C., Dinu, A., Baciu, I.-C. & Viorica, E.-D. (2015), Stochastic Models for the Inflation Rate in Romania. Transformations in Business and Economics, 14, 343-359.
Samrad, J. N., Seyyed, M. T. F. G., Mohsen, S. & Saeed, S. (2021). Annual Forecasting of Inflation Rate in Iran: Autoregressive Integrated Moving Average Modeling Approach”. Engineering Reports, 3(4). https://doi.org/10.1002/eng2.12344
Abstract views: 106