Article thumbnail

How Much Can Outlook Forecasts be Improved? An Application to the U.S. Hog Market

By Evelyn V. Colino, Scott H. Irwin and Philip Garcia

Abstract

This study investigates the predictability of outlook hog price forecasts released by Iowa State University relative to alternative market and time-series forecasts. The findings suggest that predictive performance of the outlook hog price forecasts can be improved substantially. Under RMSE, VARs estimated with Bayesian procedures that allow for some degree of flexibility and model averaging consistently outperform Iowa outlook estimates at all forecast horizons. Evidence from the encompassing tests, which are highly stringent tests of forecast performance, indicates that many price forecasts do provide incremental information relative to Iowa. Simple combinations of these models and outlook forecasts are able to reduce forecast errors by economically significant levels. The value of the forecast information is highest at the first horizon and then gradually declines.forecast, futures, models, prices, time-series, vector autoregression, Agricultural Finance,

OAI identifier:
Downloaded from http://purl.umn.edu/37620

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.

Suggested articles

Citations

  1. (1997). 2 A futures prices in an efficient market should provide forecasts of subsequent spot prices that are at least as accurate as any other forecast (Tomek
  2. (1996). 3 An estimated ratio of 0.73673 is applied to lean-hog futures prices. This factor is obtained by dividing the average weight of lean hogs (180.5) by the average weight of live hogs (245) (e.g., Sutton and Albrecht
  3. (1992). Alternative Models of Structural Change
  4. (1985). An Evaluation of the Rice Outlook and Situation Price Forecasts.”
  5. (1996). An Explanation of Hog Futures Changes.” Management Marketing Memo #333, Department of Agricultural and Applied Economics,
  6. (1979). Analysis of Economic Time Series: A Synthesis.
  7. (2006). Averaging Forecasts from VARs with Uncertain Instabilities.” Research Working Paper 06-12, Federal Reserve Bank of Kansas City, Economic Research Department.
  8. (2006). Bayesian Forecasting.”
  9. (2004). Combination of Forecasts of Output Growth in a SevenCountry Data Set.”
  10. (1997). Commodity Futures Prices as Forecasts.”
  11. (1981). Commodity Price Forecasting with Large-Scale Econometric Models and the Futures Market.”
  12. (1981). Composite Forecasting: An Application with U.S.
  13. (2003). Economic and Structural Relationships in U.S. Hog Production”.
  14. (1993). Economic Evaluation of Commodity Price Forecasting Models.”
  15. (2004). Efficient Market Hypothesis and Forecasting.”
  16. (1992). Estimating VAR Models Under Non-Stationarity and Cointegration: Alternative Approaches for Forecasting Cattle Prices.”
  17. (2006). Forecast Combinations.”
  18. (2005). Forecast Encompassing as the Necessary Condition to Reject Futures Market Efficiency: Fluid Milk Futures.”
  19. (1970). Forecasting Daily Hog Prices and Quantities:
  20. (1986). Forecasting Economic Time Series, 2 nd Edition.
  21. (1998). Forecasting Economic Time Series.
  22. (1981). Forecasting Livestock Prices with Individual and
  23. (2003). Forecasting Output and Inflation: The Role of Asset Prices.”
  24. (2005). Forecasting the Counter-Cyclical Payment Rate for U.S. Corn: An Application of the Futures Price Forecasting Model.” U.S. Department of Agriculture, Economic Research Service,
  25. (1986). Forecasting Vector Autoregressions with Bayesian Priors.”
  26. (1986). Forecasting with Bayesian Vector Autoregressions-Five Years of
  27. (2006). Forecasting with Small Macroeconomic VARs in the Presence of Instabilities.” Research Working Paper 06-09, Federal Reserve Bank of Kansas City, Economic Research Department.
  28. (1984). Forecasting with Vector Autoregressions versus a Univariate ARIMA Process: An Empirical Example with U.S.
  29. (2008). Global Agricultural Supply and Demand: Factors Contributing to the Recent Increase in Food Commodity Prices.”
  30. (2004). How Costly is it to Ignore Breaks when Forecasting the Direction of a Time Series?”
  31. (2004). Improving Forecast Accuracy by Combining Recursive and Rolling Forecasts.” Research Division, Research Working Paper 04-10, Federal Reserve Bank of Kansas City.
  32. (2000). Macroeconomic Modeling with Asymmetric Vector Autoregressions.”
  33. (2002). Market Timing and Return Prediction under Model Instability.”
  34. (1981). Modeling Multiple Time Series with
  35. (1977). Multiple Time Series: Determining the Order of Approximately Autoregressive Schemes.” Multivariate Analysis
  36. (2000). Out-of-Sample Tests of Forecasting Accuracy: An Analysis and Review.”
  37. (2007). Outlook vs. Futures: Three Decades of Evidence in Hog and Cattle Markets.”
  38. (1990). Price Forecasting with Time-Series Methods and Nonstationary Data: An Application to Monthly U.S. Cattle Prices.”
  39. (1995). System Theoretic Time-Series Forecasts of Weekly Live Cattle Prices.”
  40. (1998). Tests for Forecast Encompassing.”
  41. (2006). The Forecasting Journals and their Contribution to Forecasting Research: Citation Analysis and Expert Opinion.”
  42. (1994). The Forecasting Performance of Livestock Futures Prices: A Comparison to USDA Expert Predictions.”
  43. (1995). The Industrialization of Hog Production.”
  44. (2004). The Value of Public Price Forecasts: Additional Evidence in the Live Hog Market.”
  45. (1985). Theory of Futures Market Responses to Government Crop Forecasts.”
  46. (2003). USDA Livestock Price Forecasts: A Comprehensive Evaluation.”
  47. (1988). Vector Autoregression Forecasting Models: Recent Development Applied to the U.S.