Modeling Regime Shifts in Philippines Corn Production Using Hidden Markov Approach

This paper examines the quarterly corn production dynamics in the Philippines between 2010 and 2025 through a Hidden Markov Model (HMM) approach to determine hidden regimes of volatility. Through the modeling of production as a process driven by hidden states, the estimation delivers an evident switch between low- and high-volatility regimes, each with different mean levels and residual variances. Stationarity testing upheld the applicability of the time series to regime modeling, and model comparison with AIC, BIC, and log-likelihood statistics highly preferred the 2-state HMM to a reduced 1-state model. Transition probability estimates reflected almost deterministic switching behavior between regimes and demonstrated the cyclical pattern of agricultural production, presumably caused by seasonal, climatic, or policy-based factors. The results highlight the usefulness of regime-switching models in identifying latent structural change in farm-level data and facilitate their use in prediction, risk management, and policy formulation towards improved food security, aiding the achievement of UN Sustainable Development Goal 2 (Zero Hunger).