Abstract
Effective monetary and fiscal policy can be set with an appropriate inflation forecast. Therefore, the aim of this study is to forecast Puntland’s consumer price index (CPI) using monthly data from July, 2017 to February, 2021. The study adopted and compared different time series models including regression with ARIMA errors (ARIMAX), STL decomposition, robust exponential smoothing (ROBETS), single exponential smoothing (SES) and artificial neural network (ANN) models. Various forecast accuracy measures and information criteria such as Akaike Information Criteria (AIC), Corrected Akaike Information Criteria (AICc) and Bayesian Information Criteria (BIC) were adopted to assess the forecasting ability of these five models. The results illustrated that ANN and STL decomposition models can better forecast Puntland’s CPI. The forecast results from ANN and STL decomposition models revealed that the CPI of Puntland will slightly decline or stay constant over the forecasted period. Consistent with the result, the Ministry of Finance and the State Bank of Puntland need to keep inflation within the targeted range.
This is a preview of subscription content, log in via an institution to check access.
Source: Thoplan (2014)
Similar content being viewed by others
Availability of data and material
The data that support the findings of this study was secondary data taken from the Puntland Department of Statistics under the Ministry of Planning, Economic Development and International Corporation.
Code availability
The code that supports the findings of this study is available from the corresponding author upon reasonable request.
References
Aczel, A.D.: Complete Business Statistics. Irwin. Inc., Boston, MA (1989)
Adebiyi, A.A., Adewumi, A.O., Ayo, C.K.: Comparison of ARIMA and artificial neural networks models for stock price prediction. J. Appl. Math. 2014, 1–7 (2014). https://doi.org/10.1155/2014/614342
Ahmar, A.S., Fitmayanti, F., Ruliana, R.: Modeling of inflation cases in South Sulawesi Province using single exponential smoothing and double exponential smoothing methods. Qual. Quant. (2021). https://doi.org/10.1007/s11135-021-01132-8
Aiken, M.: Using a neural network to forecast inflation. Indus. Manage. Data Syst. (1999)
Baciu, I.C.: Stochastic models for forecasting inflation rate. Empirical evidence from Romania. Proc. Econ. Finance 20, 44–52 (2015). https://doi.org/10.1016/s2212-5671(15)00045-3
Brown, R.G., Meyer, R.F.: The fundamental theorem of exponential smoothing. Oper. Res. 9, 673–685 (1961). https://doi.org/10.1287/opre.9.5.673
Cleveland, R.B., Cleveland, W.S., McRae, J.E., Terpenning, I.: STL: a seasonal-trend decomposition. J. off. Stat. 6, 3–73 (1990)
Coibion, O., Gorodnichenko, Y., Kumar, S., Pedemonte, M.: Inflation expectations as a policy tool? J. Int. Econ. 124, 103297 (2020). https://doi.org/10.1016/j.jinteco.2020.103297
Crevits, R., Croux, C.: Forecasting using robust exponential smoothing with damped trend and seasonal components. SSRN Electron. J. (2017). https://doi.org/10.2139/ssrn.3068634
Du, Y., Cai, Y., Chen, M., Xu, W., Yuan, H., Li, T.: A novel divide-and-conquer model for CPI prediction using ARIMA, Gray Model and BPNN. Proc. Comput. Sci. 31, 842–851 (2014). https://doi.org/10.1016/j.procs.2014.05.335
Enke, D., Mehdiyev, N.: A hybrid neuro-fuzzy model to forecast inflation. Proc. Comput. Sci. 36, 254–260 (2014). https://doi.org/10.1016/j.procs.2014.09.088
Givargis, S., Karimi, H.: A basic neural traffic noise prediction model for Tehran’s roads. J. Environ. Manage. 91, 2529–2534 (2010). https://doi.org/10.1016/j.jenvman.2010.07.011
Gjika, E., Puka, L., Zaçaj, O.: Forecasting Consumer Price Index (CPI) using time series models and multi regression models (Albania case study). Finan. Eng. 466–472 (2018). https://doi.org/10.3846/bm.2018.51
Hyndman, R.J., Athanasopoulos, G.: Forecasting: principles and practice. OTexts (2018)
Kharimah, F., Usman, M., Widiarti, W., Elfaki, F.A.: Time series modeling and forecasting of the consumer price index Bandar Lampung. Sci. Int. 27, 4619–4624 (2015)
Koutsoyiannis, D.: Uncertainty, entropy, scaling and hydrological stochastics. Hydrol. Sci. J. 50, 381–404 (2005). https://doi.org/10.1623/hysj.50.3.405.65028
Koutsoyiannis, D., Yao, H., Georgakakos, A.: Medium-range flow prediction for the Nile: a comparison of stochastic and deterministic methods. Hydrol. Sci. J. 53, 142–164 (2008). https://doi.org/10.1623/hysj.53.1.142
Lidiema, C.: Modelling and forecasting inflation rate in Kenya using SARIMA and Holt-Winters triple exponential smoothing. Am. J. Theor. Appl. Stat. 6, 161–169 (2017). https://doi.org/10.11648/j.ajtas.20170603.15
Mbuli, N., Mathonsi, M., Seitshiro, M., Pretorius, J.H.C.: Decomposition forecasting methods: a review of applications in power systems. Energy Rep. 6, 298–306 (2020). https://doi.org/10.1016/j.egyr.2020.11.238
Mohamed, J.: Time series modeling and forecasting of Somaliland consumer price index: a comparison of ARIMA and regression with ARIMA errors. Am. J. Theor. Appl. Stat. 9, 143–153 (2020). https://doi.org/10.11648/j.ajtas.20200904.18
Montgomery, D.C., Johnson, L.A., Gardiner, J.S.: Forecasting and time series analysis. McGraw-Hill Companies (1990)
Nucara, A., Pietrafesa, M., Scaccianoce, G., Staltari, G.: A comparison between analytical models and Artificial neural networks in the evaluation of traffic noise levels. In: Proceedings 17th International Congress on Acoustics, ICA Rome, pp. 208–209 (2002)
Öner, C.: Inflation: Prices on the rise. International Monetary Fund (2012)
Thanh, S.D.: Threshold effects of inflation on growth in the ASEAN-5 countries: a panel smooth transition regression approach. J. Econ. Finance Admin. Sci. 20, 41–48 (2015). https://doi.org/10.1016/j.jefas.2015.01.003
Thoplan, R.: Simple v/s sophisticated methods of forecasting for mauritius monthly tourist arrival data. Int. J. Stat. Appl. 4, 217–223 (2014)
Tinoco Zermeño, M.Á., Venegas Martínez, F., Torres Preciado, V.H.: Effects of inflation on financial sector performance: new evidence from panel quantile regressions. Invest. Econ. 77, 94–129 (2018). https://doi.org/10.22201/fe.01851667p.2018.303.64156
Wang, X., Smith, K., Hyndman, R.: Characteristic-based clustering for time series data. Data Min. Knowl. Disc. 13, 335–364 (2006). https://doi.org/10.1007/s10618-005-0039-x
Zhao, L.L., Wang, B., Mbachu, J., Egbelakin, T.: Using artificial neural networks to forecast producer price index for New Zealand. Int. J. Internet Manuf. Serv. 7, 191–215 (2020). https://doi.org/10.1504/ijims.2020.107944
Funding
No funding was received to assist with the preparation of this manuscript.
Author information
Authors and Affiliations
Contributions
All authors contributed to the preparation of the manuscript. AOA had the conceptualization and wrote the introduction, literature review and methodology, collected data and organized the manuscript. JM performed data analysis and participated in the first draft preparation. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Ethics approval
This article does not contain any studies with humans and animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ali, A.O., Mohamed, J. The optimal forecast model for consumer price index of Puntland State, Somalia. Qual Quant 56, 4549–4572 (2022). https://doi.org/10.1007/s11135-022-01328-6
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11135-022-01328-6