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https://rda.sliit.lk/handle/123456789/3631
Title: | Forecasting of Constant GDP per capita of Sri Lanka using ARIMA model |
Authors: | Wijesinghe, W.R.A.N.M Umayangi, K.A.S, De Silva, H.D.K Mundigalage, S.D.M Peiris, T. S. G |
Keywords: | ARIMA models Forecasting GDP per capita |
Issue Date: | 1-Nov-2023 |
Publisher: | Faculty of Humanities and Sciences, SLIIT |
Citation: | Wijesinghe, W.R.A.N.M, Umayangi, K.A.S, De Silva, H.D.K, Mundigalage, S.D.M., Peiris, T. S. G (2023). Forecasting of Constant GDP per capita of Sri Lanka using ARIMA model. Proceedings of SLIIT International Conference on Advancements in Sciences and Humanities, 1-2 December, Colombo, pages 269-274. |
Series/Report no.: | Proceedings of the 4th SLIIT International Conference on Advancements in Sciences and Humanities; |
Abstract: | 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. |
URI: | https://rda.sliit.lk/handle/123456789/3631 |
ISSN: | 2783-8862 |
Appears in Collections: | Proceedings of the SLIIT International Conference on Advancements in Science and Humanities2023 [ SICASH] |
Files in This Item:
File | Description | Size | Format | |
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298-303 Forecasting of Constant.pdf | 1.53 MB | Adobe PDF | View/Open |
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