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.
Forecasting Inflation Rate in the Philippines Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Model (Published)
The Philippines has experienced a long period of rapid economic growth. However, inflation has now become a major concern, with the Philippines currently experiencing high inflation. High inflation is a threat to strong economic growth, particularly to people’s purchasing power. This study aims to predict the inflation rate in the Philippines using Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The historical data on the inflation rate of the Philippines, comprising 135 observations from January 2012 to March 2023, was obtained from the official website of the Philippine Statistic Authority (PSA). The study followed the Box Jenkins Methodology, which consists of 4 stages: Identification, Estimation, Diagnostic Checking, and Forecasting to develop a SARIMA model. It was then revealed that the best SARIMA model for predicting the inflation rate in the Philippines is SARIMA based on the following criteria: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Hannan-Quinn Information Criterion (HQIC), , and the Highest Log Likelihood. This chosen model has a Mean Absolute Percentage Error (MAPE) of 8.17% which is within the acceptable range of 25%. This shows an indication that the forecast is acceptably accurate. The best SARIMA model was then used to forecast the inflation rate in the Philippines from April 2023 to March 2024, and based on the result of the forecasting, it appears that the inflation rate in the Philippines is expected to decline gradually over the next 12 months, from 6.93% in April 2023 to 4.85% in March 2024. These findings could provide insights that the government can use to make decisions about monetary policies and help the economy of the Philippines to improve.
Keywords: Box Jenkins methodology, Inflation Rate, Monetary Policies, seasonal autoregressive integrated moving average (SARIMA) model.