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 12

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 2005 2006 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 ForecastLevel Confidence Limit Million bales USDA5.1-- ICAC4.4-- A5.20.1 B4.80.1 C5.00.4 D5.30.1 Average5.00.1

13. Forecasting after structural change Thousand bales Past error variances may be poor guide Genetically modified cotton increases exports Convert confidence limits to percentages of past Indian exports: 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 ForecastLevel Confidence Limit Million bales USDA5.1-- ICAC4.4-- A5.20.5 B4.80.5 C5.02.8 D5.30.5 Average5.00.7

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