AEDE’s Ani Katchova and Siddhartha Bora win Outstanding Research Award from AAEA-AFM section

Aug. 1, 2021
Pcitures of Ani Katchova and Siddhartha Bora

AEDE’s Ani Katchova and Siddhartha Bora received an Outstanding Research Award by the Agricultural Finance and Management section of the Agricultural and Applied Economics Association (AAEA-AFM). Bora, Katchova and their co-author, Todd Kuethe from Purdue University, were nominated for this award based on their publication, “The Rationality of USDA Forecasts under Multivariate Asymmetric Loss,” published in the American Journal of Agricultural Economics in 2021. The award recognizes research making significant contributions to the field of knowledge in Agricultural Finance and Management regarding the quality of research discovery, demonstrated by excellence in research methodology. The AAEA-AFM award was presented during the AAEA Annual Meeting in Austin, Texas on August 1, 2021.  
Ani Katchova is Professor and Farm Income Enhancement Chair in the Department of Agricultural, Environmental, and Development Economics (AEDE). Siddhartha Bora is a Ph.D. candidate, and this paper is a part of his dissertation work supervised by Dr. Katchova and supported by the Farm Income Enhancement Program. 

In their paper, Bora, Katchova, and Kuethe evaluate the “rationality” of two major series of USDA forecasts, the farm income forecasts released by the Economic Research Service and the World Agricultural Supply and Demand Estimates (WASDE) price and production forecasts produced by the World Agricultural Outlook Board. The study builds upon previous findings of underprediction bias in the USDA forecasts reported in several studies. The bias toward underprediction has been especially prominent in the early farm income forecasts. Forecasters may produce biased forecasts but may yet be “rational” if they minimize their loss due to forecast errors.  Using a novel multivariate asymmetric framework, the authors show that USDA forecasts are rational under the assumption that USDA places different weights on under- and over-prediction errors. This approach is different from traditional studies, which typically do not differentiate between under- and over-prediction errors by minimizing a mean square (MSE) loss. In addition, they estimate the forecaster’s loss function parameters in a joint multivariate framework that considers interactions between the losses due to errors in different variables.

The study addresses questions relevant to a wide range of players in the agricultural sector who use the USDA forecasts, including farmers, lenders, agricultural business leaders, and agricultural policymakers. These findings shed light on “why” the USDA forecasters may produce biased forecasts. Another implication of the findings is that they may help inform future revisions of USDA forecast models and procedures.  Finally, while the results are mostly consistent with previous findings, they yield an alternative interpretation of the results of prior research.  

Bora, S.S., A.L. Katchova, and T.H. Kuethe. “The Rationality of USDA Forecasts under Multivariate Asymmetric Loss.” American Journal of Agricultural Economics (2020),