Home⇛GRADUATE FORUM⇛vol. 18 no. 1 (2024)

Intervention Effect Using INGARCH Model on Tuberculosis Data

Marvin A. Ballena | Laurence P Usona

Discipline: Statistics

 

Abstract:

The primary focus is to validate the use of INGARCH model by satisfying its assumptions and applying the methodology in real-life case of tuberculosis (TB) data. Satisfying the assumptions for using INGARCH is the first step which uses several tools like ARCH Effect Test, Unit Root Test, and visually looking at cluster volatility. Once assumptions were met that INGARCH model is indeed viable, the study then focuses on identifying the type of model to use by evaluating Residual—Auto-Correlation Function (ACF), Assessing Predictive Model—Probability Integral Transform (PIT) histogram, Marginal Calibration, Sharpness, and Selecting model criteria—Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Quasi Information Criterion (QIC). Once model is chosen according to parameters, the identification of intervention effects is done and created a final model. Negative Binomial is the final model that had successfully identified and corrected four intervention effects—two level shifts and two transient shifts. The corrected model was used to forecast a year’s worth of TB cases in the Philippines averaging 933 cases/ month for 2020. It is highly recommended to develop new forecasting methodology that would accommodate extreme natural events like COVID-19 which severely impacted the actual number of cases since its detection.



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