Time Series Analysis of Factors Affecting Coconut Price in the Philippines
DOI:
https://doi.org/10.69569/jip.2025.618Keywords:
Coconut production, Copra price, Market demand, Rainfall variability, Time series analysisAbstract
Coconut remains a fundamental agricultural product in the Philippines, widely used in food production, cosmetics, and bio-industries. Despite its importance, the price of copra, its dried kernel, has experienced substantial volatility due to environmental conditions, market dynamics, and production levels. This study aims to analyze and forecast the factors influencing copra prices in the country, utilizing time series data from 2013 to 2022. Specifically, it investigates the influence of rainfall, coconut production, and market demand. Utilizing a non-experimental quantitative approach, the study employed ARIMA and SARIMA models for trend analysis and forecasting. AIC, BIC, AICc, residual variance, and log-likelihood guided model selection. Results revealed that ARIMA(0,1,1) was optimal for forecasting coconut production, ARIMA(2,1,1) for copra prices, and SARIMA(2,0,1)(1,0,1)[12] for Philippine rainfall,SARIMA(0,0,1)(2,1,1)[12] was used for Luzon, SARIMA(1,0,0)(2,0,0)[12] for Visayas, ARIMA(1,1,1) for Mindanao. Regression results confirmed that rainfall did not significantly influence coconut production, and neither coconut production nor market demand had a statistically significant effect on copra prices. The forecast indicated a potential decline in coconut production and copra prices if current conditions persist. The study concludes that time series forecasting models are essential for agricultural stakeholders, enabling data-driven planning, risk management, and policy formulation. To improve predictive accuracy and policy relevance, future studies are encouraged to incorporate historical data points on market demand and coconut production, and to adopt multivariate time series models, such as ARIMAX or Vector Autoregression (VAR), which can account for external shocks and global economic indicators. These approaches could better capture the complex interactions within the Philippine coconut industry and support more effective decision-making.
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