Modelling Rice Yield in the Philippines using Dynamic Spatio-Temporal Models

Recent unpredictable and extreme weather episodes and infestation are some of the realistic occurrences of structural change in the agricultural system which produce outliers and extreme values in our data, and consequently pose problems when building statistical models. An estimation procedure which is robust to structural change is therefore necessary. Three spatial-temporal models with varying dynamic characteristics of the parameters are postulated each with a different estimation procedure for the agricultural yield in irrigated areas of the Philippines. One of which is a robust estimation procedure using forward search algorithm with bootstrap in a backfitting algorithm. The other two algorithms also used the backfitting algorithm but infused with the CochranneOrcutt procedure. The robust estimation procedure and the other one which considers varying parameter across space gave competitive predictive abilities and are better than the ordinary linear model. Simulation studies show the superiority of the robust estimation procedure over the Cochranne-Orcutt procedure and ordinary linear model in the presence of structural change.