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Testing the optimality of USDA's WASDE forecasts under unknown loss

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  • Kexin Ding
  • Ani L. Katchova

Abstract

Motivated by the long‐lasting debate on whether the United States Department of Agriculture's (USDA's) World Agricultural Supply and Demand Estimates (WASDE) forecasts are optimal, we employ an unknown loss method for ex post evaluation which assumes that the USDA forecasters' loss function is unknown. We conduct optimality tests of the WASDE forecasts for corn, soybeans, and wheat published during 1988–2019. Our results suggest that USDA forecasters generally realize optimality during the data‐generating process. Our findings are consistent with previous studies when narrowing down the more general unknown loss function to the symmetric or asymmetric loss function which assumes a specific shape for the loss function. This study provides implications based on the unknown loss function that the USDA forecasters can boost their information set as an alternative way to improve the WASDE forecasts. [EconLit Citations: D84, E37, Q13, Q14].

Suggested Citation

  • Kexin Ding & Ani L. Katchova, 2024. "Testing the optimality of USDA's WASDE forecasts under unknown loss," Agribusiness, John Wiley & Sons, Ltd., vol. 40(4), pages 846-865, October.
    • Handle: RePEc:wly:agribz:v:40:y:2024:i:4:p:846-865
    DOI: 10.1002/agr.21850
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