Modelling Consumer Price Index in Tanzania: Holt Winter’s Approach

Gasper, Laban; Ernest, Enid Kebby

doi:10.5281/zenodo.8418331

Document Type : Original Research

Authors

Laban Gasper ¹
Enid Kebby Ernest ²

¹ Department of ICT and Mathematics, College of Business Education (CBE) P. O. Box 2077, Dodoma, Tanzania

² Department of Procurement & Logistics, Tanzania Institute of Accountancy (TIA), Mwanza, Tanzania

https://doi.org/10.5281/zenodo.8418331

Abstract

Consumer price index (CPI) is a socioeconomic statistic that tracks changes over time in the average price of consumer goods and services such as household purchases of fuel, transportation, food and so on that consumers buy, use, or pay for. The purchasing power of everyone is impacted by rising costs, especially if salaries stay the same. Our ability to purchase more things with our TZS reduces when the CPI increases more quickly than earnings, which has an impact on our cost of living. The aim of this study is to use the CPI monthly data from IMF website for the period from Jan 2010 to Dec 2022 to develop a forecasting model by using Holt Winter’s approach. Holt Winter's model based on four equations and popularly known as Triple exponential smoothing is commonly used in forecasting data with trends and seasonality. Holt Winter’s model is composed of four equations relating to level, trend, seasonal and forecast. The results revealed that the Holt winter’s model with smoothing parameters, 0.9 for level, 0.12 for trend, and 0.03 for seasonal was the best model in forecasting Consumer Price Index. The CPI for Tanzania is predicted for the next eighteen months and it has been observed that the trend of CPI is likely to increase in the next eighteen months.

Keywords

20.1001.1.23832126.2023.10.8.2.9

Main Subjects

©2023 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.

References

Aabeyir, B. (2019). Forecasting The Consumer Price Index Of Ghana Using Exponential Smoothing Methods. Mathematical Theory and Modeling.

Ali, A., & Mahgoub, A. (2020). Prediction of CPI in Saudi Arabia: Holt’s Linear Trend Approach. Research in World Economy, 11(6), 302. https://doi.org/10.5430/rwe.v11n6p302

Arindam Banerjee. (2021). Forecasting Price Levels in India—An Arima Framework. 25(1).

Corpin, S. J. T., Marbella, J. N. P., Kua, S. J. J., Mabborang, R. C., & Lamprea, C. T. (2023). Forecasting Inflation Rate in the Philippines Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Model. European Journal of Computer Science and Information Technology, 11(2), 13–36. https://doi.org/10.37745/ejcsit.2013/vol11n21336

Efrilia, I. (2021). Comparison of ARIMA and Exponential Smoothing Holt-Winters Methods for Forecasting CPI in The Tegal City, Central Java. Jurnal Ekonomi Pembangunan, 19(02), 97–106. https://doi.org/10.22219/jep.v19i02.18040

Iftikhar, N. (2013). Forecasting the Inflation in Pakistan; The Box-Jenkins Approach.

Kelikume, I., & Salami, A. (2014). Time Series Modeling and Forecasting Inflation: Evidence From Nigeria. 8(2).

Lidiema, C. (2017). Modelling and Forecasting Inflation Rate in Kenya Using SARIMA and Holt-Winters Triple Exponential Smoothing. American Journal of Theoretical and Applied Statistics, 6(3), 161. https://doi.org/10.11648/j.ajtas.20170603.15

Mia, S. (2019). Modelling and Forecasting the Consumer Price Index in Bangladesh through Econometric Models. 59(1).

Mohamed, J. (2020). Time Series Modeling and Forecasting of Somaliland Consumer Price Index: A Comparison of ARIMA and Regression with ARIMA Errors. American Journal of Theoretical and Applied Statistics, 9(4), 143. https://doi.org/10.11648/j.ajtas.20200904.18

Molebatsi, K., & Raboloko, M. (2016). Time Series Modelling of Inflation in Botswana Using Monthly Consumer Price Indices. International Journal of Economics and Finance, 8(3), 15. https://doi.org/10.5539/ijef.v8n3p15

Muhammed, K. A., Bolarinwa, F. A., & Ajao, I. O. (2019). Exponential Smoothing Methods In Forecasting Nigeria Consumer Price Index.

Ngailo, E., Luvanda, E., & Massawe, E. S. (2014). Time Series Modelling with Application to Tanzania Inflation Data. Journal of Data Analysis and Information Processing, 02(02), 49–59. https://doi.org/10.4236/jdaip.2014.22007

Nnamdi, E., Odunayo, B. J., Olawale, A. I., & Olurin, A. (2021). Comparative Study Of Some Time Series Models On The Monthly Consumer Price Index (Cpi) Of Consumer Goods In Nigeria.

Nyoni, T. (2019). Modeling and forecasting inflation in Tanzania using ARIMA models. https://mpra.ub.uni-muenchen.de/92458/

Ospina, R., Gondim, J. A. M., Leiva, V., & Castro, C. (2023). An Overview of Forecast Analysis with ARIMA Models during the COVID-19 Pandemic: Methodology and Case Study in Brazil. Mathematics, 11(14), 3069. https://doi.org/10.3390/math11143069

Prapcoyo, H., & As’ad, M. (2022). The Forecasting Of Monthly Inflation In Yogyakarta City Uses An Exponential Smoothing-State Space Model. International Journal of Economics, Business and Accounting Research (IJEBAR), 6(2), 800. https://doi.org/10.29040/ijebar.v6i2.4853

Rohmah, M. F., Putra, I. K. G. D., Hartati, R. S., & Ardiantoro, L. (2021). Comparison Four Kernels of SVR to Predict Consumer Price Index. Journal of Physics: Conference Series, 1737(1), 012018. https://doi.org/10.1088/1742-6596/1737/1/012018

Shinkarenko, V., Hostryk, A., Shynkarenko, L., & Dolinskyi, L. (2021). A forecasting the consumer price index using time series model. SHS Web of Conferences, 107, 10002. https://doi.org/10.1051/shsconf/202110710002

Wanto, A., Fauzan, M., Suhendro, D., Parlina, I., Damanik, B. E., Siregar, P. A., & Hidayati, N. (2018). Epoch Analysis and Accuracy 3 ANN Algorithm using Consumer Price Index Data in Indonesia: Proceedings of the 3rd International Conference of Computer, Environment, Agriculture, Social Science, Health Science, Engineering and Technology, 35–41. https://doi.org/10.5220/0010037400350041

Zhang, X. (2023). Forecast and Analysis of China’s CPI Based on SARIMA Model. In D. Qiu, Y. Jiao, & W. Yeoh (Eds.), Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022) (Vol. 5, pp. 1354–1361). Atlantis Press International BV. https://doi.org/10.2991/978-94-6463-030-5_135

International Journal of Management, Accounting and Economics

Modelling Consumer Price Index in Tanzania: Holt Winter’s Approach

References

References

Volume 10, Issue 8 - Serial Number 109
August 2023
Pages 538-550

Modelling Consumer Price Index in Tanzania: Holt Winter’s Approach

References

References

Volume 10, Issue 8 - Serial Number 109August 2023Pages 538-550

Volume 10, Issue 8 - Serial Number 109
August 2023
Pages 538-550