Modeling and forecasting global oil price on Sri Lankan inflation rate

Abstract

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.