European Journal of Computer Science and Information Technology (EJCSIT)

EA Journals

mean absolute percentage error

Forecasting Gross Domestic Product in the Philippines Using Autoregressive Integrated Moving Average (ARIMA) Model (Published)

Gross domestic product (GDP) plays a vital role in providing valuable insights into the size and performance of an economy. The GDP in the Philippines has shown steady growth over the years, reflecting the country’s economic development and progress. This paper presents a GDP forecast for the next eight years in the Philippines using Autoregressive Integrated Moving Average (ARIMA) model. This study aims to develop an optimal ARIMA model using the Box-Jenkins Methodology, incorporating a range of tests and selection criteria. The ARIMA (1,2,1) model is a valid choice for forecasting GDP in the Philippines, supported by its accuracy, as evidenced by the acceptable MAPE and high R-squared value. The model successfully captures patterns and trends in the GDP data, despite the significant variability represented by the sigma-squared value. The forecasted GDP for 2022-2029 suggests a positive outlook with a steady growth trajectory. These findings have important implications for economic planning, policy-making, and decision-making in the Philippines, as the forecasted GDP provides insights into the country’s future growth and development, influencing investment decisions, government strategies, and overall economic stability.

Keywords: Box Jenkins methodology, Forecast, Gross Domestic Product (GDP), autoregressive integrated moving average (ARIMA), mean absolute percentage error, time series analysis.

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