Friday, March 25, 2011
Victoria Lazariu, Chengxuan Yu & Craig Gundersen: "Forecasting Women, Infants, and Children Caseloads: A Comparison of Vector Autoregression and Autoregressive Integrated Moving Average Approaches"
Victoria Lazariu, Chengxuan Yu and Craig Gundersen posted "Forecasting Women, Infants, and Children Caseloads: A Comparison of Vector Autoregression and Autoregressive Integrated Moving Average Approaches" on SSRN:
Under the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), each state receives a fixed federal grant for the operation of WIC in the upcoming federal fiscal year. Accurate forecasting is vital because states have to bear the expenses of any underestimation of WIC expenditures. Using monthly data from 1997 through 2005, this paper examined the performance of two competing models, autoregressive integrated moving average (ARIMA) and vector autoregression (VAR), in forecasting New York WIC caseloads for women, infants, and children. VAR model predicted over $120,000 less per month in forecast errors in comparison to the ARIMA model.