Poisson spatial autoregression modelling of poverty count data in the Philippines - Bañez, John Erwin (2012)
Count data with skewed distribution and possible spatial auto-regression (SAR) often causes difficulty in modelling. Violations on the assumptions in ordinary least squares (OLS) may occur. While Poisson regression can offer some remedy in modelling count data, it still does not take into account the spatial dependencies of the data. This paper uses general linear estimation via backfitting algorithm in Poisson-SAR of poverty count in the Philippines for 2000. The model is assessed based on comparison from other models and the actual poverty count (MAPE and poverty map). MAPE was lowest in Poisson-SAR compared to other models.
Keywords: Poverty statistics; Spatial systems