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Integrating Judgmental and Quantitative Forecasts

Integrating Judgmental and Quantitative Forecasts. Stephen MacDonald, ERS/USDA Research Center on Forecasting Seminar January 17, 2007. Introduction. Futures markets often find USDA’s forecasts crucial Resource constraints have reduced the staff-years USDA has for forecasting

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Integrating Judgmental and Quantitative Forecasts

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  1. Integrating Judgmental and Quantitative Forecasts Stephen MacDonald, ERS/USDA Research Center on Forecasting Seminar January 17, 2007

  2. Introduction • Futures markets often find USDA’s forecasts crucial • Resource constraints have reduced the staff-years USDA has for forecasting • Quantitative methods can be used to supplement USDA’s traditionally judgmental forecasts • Example: international commodity trade

  3. USDA forecasts: Two perspectives 1 2

  4. Overview of USDA Forecasts • National Agricultural Statistics Service (NASS) estimates U.S. production of more than 100 commodities • 7 of these commodities have been legislatively deemed “market sensitive” • Wheat, corn, soybeans, cotton, citrus, cattle, and hogs • Since 1973, USDA has published demand forecasts as well • Interagency Commodity Estimates Committees

  5. Interagency Commodity Estimates Committee (ICEC) • ICEC comprised of: Economic Research Service (ERS) Foreign Agricultural Service (FAS) Farm Service Agency (FSA) Agricultural Marketing Service (AMS) World Agricultural Outlook Board (WAOB) ,chair • Methodology of the ICEC: “A consensus…approach is used to arrive at supply and demand estimates. Consensus forecasts employ ‘models’ of all types, formal and informal.”

  6. February 2007 example: India 2006/07 cotton exports • Forecasts available from several sources: • 4.2 million bales (mb): U.S. embassy (Delhi) • 3.9 mb: India Cotton Advisory Board • 4.1 mb: International Cotton Advisory Committee • “USDA forecast too high”: personal communication from industry analysts • No actual data was available—Indian official trade data is significantly lagged • USDA’s forecast: 5 mb

  7. January 2008: India exports • 10 months of marketing year data published • Averaged 437,000 bales per month • Compared to 2006, Aug-May trade is: • 1.3 m. bales higher • 44 % higher • During previous 4 years: • Aug-May was 84% of year Thousand bales 2006 2005 Data available through May 2007

  8. Declining resources: FAS & ERS

  9. Changing Forecasting Environment • A consensus (Delphi) approach is resource-intensive: expertise– or labor–intensive • Falling cost of data-processing and acquisition can offset reduced staffing • Timely international commodity trade data commercially available • replacing embassy reports

  10. Import forecasts based on data through month x

  11. Empirical confidence intervals • Assume future errors distributed same as past • Assume errors are normally distributed, with mean of zero • Calculate a 90 percent confidence interval for each forecast using estimated variance and t distribution • Variance estimated with past forecasts errors

  12. Alternative forecasts for India exports • Weight forecasts by inverse of confidence interval • Analysis of trade data corroborates USDA • But: international organization has forecast outside of 90% confidence interval

  13. Forecasting after structural change • Past error variances may be poor guide • Genetically modified cotton increases exports • Convert confidence limits to percentages of past Indian exports: Thousand bales Forecasts • Example • 100,000 / 837,000 = 12 % • 0.12 * 5.0 mb = 0.6 mb, alternative confidence limit 837,000 bales = 99-05 average

  14. Forecasts: adjusted confidence limits • Proportional confidence limit suggests ICAC forecast is not incompatible with published trade data • However, actual exports totaled 4.4 mb already • Alternative adjustments may be more appropriate

  15. Integration with judgmental forecasts • Confidence intervals expand compatibility of quantitative estimates with market intelligence from embassies and industry • Also provide weights for combining forecasts—add intuitive appeal • Can be incorporated into rules of thumb to guide judgmental decision-making

  16. Integration with judgmental forecasts

  17. Conclusion • USDA forecasting is increasingly substituting “capital” for labor • We are exploring how to most efficiently exploit the growing availability of data • We are determining how best to integrate these quantitative forecasts into USDA’s judgment-based system

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