A Quarterly Econometric Model for Short-Term Forecasting of the U.S. Dairy Industry
by Roberto Mosheim
Technical Bulletin No. (TB-1932) 38 pp, January 2012
Cover image for TB1932 This research evaluates the econometric approaches employed by USDA’s Economic Research Service (ERS) to contribute to the dairy sector forecasts published in the monthly World Agricultural Supply and Demand Estimates (WASDE) report. To generate the estimates, a quarterly model of the U.S. dairy industry is specified using data for fourth-quarter 1998 (Q4/1998) to first quarter 2009 (Q1/2009), and it is estimated and validated employing data for Q2/2009 to Q1/2010. Different forecasts are generated using a variety of single equation and system methods, and which are then evaluated in terms of forecast precision or predicting turning points in the data. Different approaches, however, more effectively forecast different variables. Vector autoregression with exogenous variables outperforms structural regression models when forecasting prices, but single and system estimations of structural models are superior to time series models when forecasting some items in farm supply and commodity balance sheets.
Keywords: U.S. dairy industry, forecasts, simultaneous equations, vector autoregression
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