Volume 9, Issue 4, July 2020, Page: 143-153
Time Series Modeling and Forecasting of Somaliland Consumer Price Index: A Comparison of ARIMA and Regression with ARIMA Errors
Jama Mohamed, Faculty of Mathematics and Statistics, College of Applied and Natural Science, University of Hargeisa, Hargeisa, Somaliland
Received: Jun. 11, 2020;       Accepted: Jun. 22, 2020;       Published: Jul. 13, 2020
DOI: 10.11648/j.ajtas.20200904.18      View  171      Downloads  152
In recent years, the Consumer Price Index (CPI) prediction has attracted the attention of many researchers due to its excellent measurement of macroeconomic performance. It is an important index that is used to measure the rate of inflation or deflation of commodities. In this paper, Autoregressive Integrated Moving Average (ARIMA) and regression with ARIMA errors, where the covariate is the time, were compared to forecast Somaliland Consumer Price Index using monthly time series data from 2013 – 2020. The study used and applied both models to produce the necessary forecasts. Also, Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (AICc), Bayesian Information Criterion (BIC) and other model accuracy measures were used to measure model’s predictive ability. By utilizing these methods, it is obtained that ARIMA (0, 1, 3) is the most suitable model for predicting CPI in Somaliland. Furthermore, the diagnostic tests show that the model presented is reliable and appropriate for forecasting Somaliland CPI data. The study results obviously indicate that CPI in Somaliland is more likely to proceed on an upward trend in the coming year. The study guides policymakers to use strict monetary and fiscal policy measures to address Somaliland’s inflation.
Augmented Dickey-Fuller Test, Autocorrelation, Autoregressive Integrated Moving Average (ARIMA), Consumer Price Index (CPI), Ljung-Box Test
To cite this article
Jama Mohamed, 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. Vol. 9, No. 4, 2020, pp. 143-153. doi: 10.11648/j.ajtas.20200904.18
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