Abstract

http://ssrn.com/abstract=1738112
 
 

References (18)



 


 



Forecasting Women, Infants, and Children Caseloads: A Comparison of Vector Autoregression and Autoregressive Integrated Moving Average Approaches


Victoria Lazariu


affiliation not provided to SSRN

Chengxuan Yu


affiliation not provided to SSRN

Craig Gundersen


U.S. Department of Agriculture (USDA); Iowa State University

January 10, 2011

Contemporary Economic Policy, Vol. 29, No. 1, pp. 46-55, 2011

Abstract:     
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.

Number of Pages in PDF File: 10

JEL Classification: H7, C5

Accepted Paper Series


Date posted: January 12, 2011  

Suggested Citation

Lazariu, Victoria and Yu, Chengxuan and Gundersen, Craig, Forecasting Women, Infants, and Children Caseloads: A Comparison of Vector Autoregression and Autoregressive Integrated Moving Average Approaches (January 10, 2011). Contemporary Economic Policy, Vol. 29, No. 1, pp. 46-55, 2011. Available at SSRN: http://ssrn.com/abstract=1738112 or http://dx.doi.org/10.1111/j.1465-7287.2010.00203.x

Contact Information

Victoria Lazariu (Contact Author)
affiliation not provided to SSRN ( email )
Chengxuan Yu
affiliation not provided to SSRN ( email )
Craig Gundersen
U.S. Department of Agriculture (USDA) ( email )
1301 New York Ave. NW
Washington, DC 20250
United States
Iowa State University ( email )
Ames, IA 50011
United States
Feedback to SSRN


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References:  18

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