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Modeling Regime Shifts in Philippines Corn Production Using Hidden Markov Approach

Author

Listed:
  • Vicente Salvador E. Montaño

    (College of Business Administration Education, University of Mindanao, Davao City, Davao Del Sur, Philippines)

  • Honey Grace B. Bagtasos

    (College of Business Administration Education, University of Mindanao, Davao City, Davao Del Sur, Philippines)

  • Bai Budtong A. Sakal

    (College of Business Administration Education, University of Mindanao, Davao City, Davao Del Sur, Philippines)

Abstract

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).

Suggested Citation

  • Vicente Salvador E. Montaño & Honey Grace B. Bagtasos & Bai Budtong A. Sakal, 2025. "Modeling Regime Shifts in Philippines Corn Production Using Hidden Markov Approach," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(15), pages 1058-1071, August.
    • Handle: RePEc:bcp:journl:v:9:y:2025:issue-15:p:1058-1071
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    References listed on IDEAS

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    1. Scott, Steven L. & James, Gareth M. & Sugar, Catherine A., 2005. "Hidden Markov Models for Longitudinal Comparisons," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 359-369, June.
    2. Matthew Wang & Yi-Hong Lin & Ilya Mikhelson, 2020. "Regime-Switching Factor Investing with Hidden Markov Models," JRFM, MDPI, vol. 13(12), pages 1-15, December.
    3. Mr. Ippei Shibata, 2019. "Labor Market Dynamics: A Hidden Markov Approach," IMF Working Papers 2019/282, International Monetary Fund.
    4. Kai Zheng & Yuying Li & Weidong Xu, 2021. "Regime switching model estimation: spectral clustering hidden Markov model," Annals of Operations Research, Springer, vol. 303(1), pages 297-319, August.
    5. Maheu, John M. & Yang, Qiao, 2016. "An infinite hidden Markov model for short-term interest rates," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 202-220.
    6. Steven Scott, 2011. "Data augmentation, frequentist estimation, and the Bayesian analysis of multinomial logit models," Statistical Papers, Springer, vol. 52(1), pages 87-109, February.
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