Browsing by Author "Wijesekara, J. M. C. D."

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Modeling and forecasting global oil price on Sri Lankan inflation rate
(Faculty of Science, University of Kelaniya Sri Lanka, 2024) Priyadarshana, D. A. D. S.; Wijesekara, J. M. C. D.; Chandrasekara, N. V.
Inflation serves as a key indicator of overall economic well-being. Indeed, inflation is the continuing increase in the general level of prices for goods and services over time. Moderate inflation may connote high economic growth, while high inflation is usually damaging to both long-term economic growth and financial stability. Since 1977, Sri Lanka has been undergoing continuous inflationary pressure due to power outages, energy shortages, reduced production in the agricultural sector, and others. Furthermore, the prices of the World oil market have been fluctuating, owing to changes in the taxes of crude oil, costs of refining and transport, and other related factors. All these dynamics bear directly on Sri Lanka's inflation. Therefore, policymakers, corporate leaders, and the general public needed to understand the dynamics of inflation. Therefore, this study brings out a research gap in the study of the impact of Gasoline Unl 92 (PATROL) and Gasoil 500ppm (DIESEL) in the Singapore World Oil Market on the inflation rate in Sri Lanka (NCPI). This is the novelty of this research since previous studies have not covered it. The main objective of this study is to develop a predictive model illustrating the influence of global oil prices on the inflation rate in Sri Lanka. Data for this research was gathered monthly from the Central Bank of Sri Lanka and the Singapore Platts, covering the period from January 2015 to December 2021. Moderate relationships were observed among the inflation rate and prices of Gasoline Unl 92 and Gasoil 500ppm from the Pearson correlation matrix. All the time series variables have been made stationary through log transformation and first differencing, which were checked through the ADF, PP, and KPSS tests. Assumptions in the residual diagnostics procedure of the time series regression model have not violated the characteristics of the absence of multicollinearity, autocorrelation, serial Correlation, and heteroscedasticity among the residuals. In addition, the residuals are normally distributed. The final predictive model ∆[𝑙𝑜𝑔(𝑁𝐶𝑃𝐼)]𝑡 = 0.0040 + 0.3453 ∆[𝑙𝑜𝑔(𝑁𝐶𝑃𝐼)]𝑡-1 + 0.4247 ∆[𝑙𝑜𝑔(𝑁𝐶𝑃𝐼)]𝑡-3 + 0.0511 ∆[𝑙𝑜𝑔(𝐷𝐼𝐸𝑆𝐸𝐿)]𝑡 + 0.0355 ∆[𝑙𝑜𝑔(𝑃𝐴𝑇𝑅𝑂𝐿)]𝑡 + 0.0283 ∆[𝑙𝑜𝑔(𝑃𝐴𝑇𝑅𝑂𝐿)]𝑡-1 included lagged terms of past inflation and gasoline and gas oil prices, which ended up quite accurate; given the RMSE value was 1.729, the MAE value came to 1.289, and the MAPE value was 0.901. Further validation of the strength of the model was in the 𝑅2 value of 53%. This model can underline the strong influences of world oil prices in determining Sri Lanka's inflation dynamics at the same time. Also, this only considered the global oil price of petrol and diesel because of the inflation rate of Sri Lanka, and all the other factors were limited. It can give important facts for policymakers to devise appropriate strategies for the management of inflation in the economy. Future researchers can improve this model using different methodological approaches and consider more designs for the global oil market decision.
A quantitative analysis of fishery industry in modelling of production, trade dynamics, and COVID-19 impact estimation
(Faculty of Science, University of Kelaniya Sri Lanka, 2023) Sandamini, R. P. K.; Tharaka, K. D. R.; Wickramasinghe, W. A. P. K. V.; Mendis, T. A. B.; Wijesekara, J. M. C. D.; Lakruwan, J. M. D. C.; Dasanayaka, O. R. G. K.; Liyanage, U. P.; Hewaarachchi, A. P.
Sri Lanka, being an island, is granted an immense maritime boundary. The fishery industry is a prominent and significant part of the Sri Lankan economy, contributing around 1.5% of the Gross Domestic Product. The objectives of this research are to identify the factors affecting fish production, analyse the Net Trade Balance (cost difference between import and export of fishery products) in the fishery industry, and estimate the impact of COVID-19 on fish production. The monthly data from 2015 to 2022 is taken from the Statistics unit of the Ministry of Fisheries, Sri Lanka. This data is analysed, and the findings revealed that fish production in the country had decreased significantly over time, and thereby, it has impacted the import quantities as well. Further, almost 80% of the total fish production of the country is contributed by marine fish production and 20% by inland fish production. The variations in fish production are largely caused by the inland fish harvest; that is marine fish production has monotone dynamics. Concerning the seasonal pattern, less production is visible during May and June due to the monsoon. However, a clear upward trend in the inland fish harvest is visible during this time due to the renewal of freshwater. According to a recent survey in the Indian Ocean, the decrement in the fish population and habitats contributed to the lower harvest of fish production. Further, it has been identified the factors of fuel prices, fishing gear costs, ice cube prices, and unauthorized fish catch by foreign fishermen, as the other affecting factors in fish production. Concerning fisheries export, Tuna fish and prawns play a vital role in the export market. Despite the marine resources, Sri Lanka still imports fisheries products by spending foreign remittances. The Net Trade Balance (NTB) of fisheries products in Sri Lanka is investigated in this research and modelled by multiple linear regression models (net trade balance as the response variable, and harvest of 10 fish types as independent variables, based on significance) for pre and post, COVID-19 pandemic conditions. Further, the models can accurately predict the NTB (Pre-COVID model R2 = 72.4%, post-COVID model R2 = 80.6%). This model can be used in policy and strategy analysis by respective authorities such as the Ministry of Fisheries, Sri Lanka. Using the time series methods (Moving Average, Exponential Smoothing, and SARIMA), fish production is analysed. Combining these models, the impact of the COVID-19 pandemic on fish production from February to August 2020 is estimated at 15.81%. In conclusion, this research identified the fish production patterns, COVID-19's impact on production, and a model to estimate NTB, which also can be an analytical tool for the policymaking of the fisheries industry.