SLIIT International Conference on Advancements in Sciences and Humanities [SICASH] 2023

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    PublicationOpen Access
    Forecasting Consumer Price Index in the United States
    (Faculty of Humanities and Sciences, SLIIT, 2023-11-01) Witharana, W. W. S. K; Udugama, U. K. D. T. N.; Fernando, P. M. R.,; Kaumadi, H. M. H.; Peiris, T. S. G.
    This report presents the Auto-Regressive Integrated Moving Average (ARIMA) model for forecasting the consumer price index (CPI) in US using monthly data from March 2010 to March 2023. The original series was not stationary, but the first difference series was found to be stationary using the Augmented Dicky Fuller test. The best-fitted model was identified based on the significance of the parameters, volatility (sigma2), log-likelihood, Akaike, Schwartz, and Hannan- Quinn information criterion. Parameters of the fitted model are significantly deviated from zero. The stability of the model has been checked using the roots of the unit root test. Residuals of the fitted model satisfied the randomness but nonconstant variance. The monthly forecasted values of CPI from April 2023 to August 2023 are 301.833, 302.444, 303.038, 303.639, and 304.261. The percentage errors of the forecasted values are less than one percent. This method and results provide useful information to policy and market makers for their planning,
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    PublicationOpen Access
    Forecasting of Constant GDP per capita of Sri Lanka using ARIMA model
    (Faculty of Humanities and Sciences, SLIIT, 2023-11-01) Wijesinghe, W.R.A.N.M; Umayangi, K.A.S,; De Silva, H.D.K; Mundigalage, S.D.M; Peiris, T. S. G
    GDP per capita is a global measurement for assessing the economic prosperity of nations. Constant (Real) GDP per capita eliminates the effects of inflation which allows for a more accurate comparison of GDP per capita over time. However, no statistical models have been developed to predict annual constant GDP per capita (CGDPC) in Sri Lanka. In this study, ARIMA (1,1,0) model was developed using past data from 1961 to 2018 to forecast CGDPC. The best-fitted model was identified based on three possible models using sample ACF and sample PACF of the stationary series and comparing statistics such as AIC, BIC, maximum log-likelihood, and volatility. The residuals of the fitted model were white noise. The training dataset has percentage errors ranging from -6.50% to 3.80%. The model was validated for observed data in 2019, 2020, and 2021. The percentage error for the three points were -3.49, -6.10, and 1.49 respectively. The forecasted values for 2022, 2023, and 2024 obtained were 4506.728, 4653.895, and 4810.505 respectively showing that Sri Lanka’s economy is expected to grow due to the increase in CGDPC. The GDP per capita growth rates of 2.99%, 3.27%, and 3.37% for the next 3 years also confirm this. The results obtained from this study can be effectively used for better planning. However, it is recommended to improve the model further to reduce the percentage of errors using the ARIMAX approach.