The Rationality of USDA Forecasts under Multivariate Asymmetric Loss

Abstract

A large number of previous studies suggest that many USDA forecasts are biased and/or inefficient. These findings, however, may be the result of the assumed loss function of USDA forecasters. We test the rationality of the USDA net cash income forecasts and the WASDE production and price forecasts between 1988 and 2018 using a flexible multivariate loss function that allows for asymmetric loss and non‐separable forecast errors. Our results provide robust evidence that USDA forecasters are rational expected loss minimizers yet demonstrate a tendency to place a greater weight on under‐ or overprediction. As a result, this study provides an alternate interpretation of previous findings of forecast irrationality.

Using a machine learning approach and big data to augment WASDE forecasts: Empirical evidence from US corn yield

“…The report contains information on agricultural production, consumption, inventories, trade, and prices. The industry closely follows these reports, and there is evidence that commodity markets respond to WASDE estimates (Bora et al, 2021;Dorfman & Karali, 2015;Fortenbery & Sumner, 1990;Isengildina-Massa et al, 2008;Karali et al, 2019;McKenzie, 2008;Summer & Mueller, 1989). NASS crop yield forecasts for corn and soybeans are released in the WASDE report from August to November.…”

“…The US Department of Agriculture (USDA) has two of 13 federal government forecasting agencies: the National Agricultural Statistics Service (NASS) and the Economic Research Service (ERS). These agencies account for approximately 8% of the $3.2 billion annual budget allocated to federal forecasting agencies (Bora et al, 2021). The World Agricultural Supply and Demand Estimates (WASDE) report is an essential monthly forecast report prepared and released by the USDA's World Agricultural Outlook Board (WAOB).…”

Asymmetry and interdependence when evaluating U.S. Energy Information Administration forecasts

“…2 Evaluation methods assuming asymmetric loss functions are used to evaluate the rationality of EIA forecasts in for example Auffhammer (2007) and Mamatzakis and Koutsomanoli-Filippaki (2014). However, prior to this paper, methods which account for interdependence between forecasts, applied by Caunedo et al (2020) to evaluate forecasts produced by the Federal Reserve and by Bora et al (2021) to evaluate the forecasts of the U.S. Department of Agriculture, have yet to be used on EIA forecasts. Our emphasis is on modelling interdependence, as well as asymmetries, between the EIA forecasts of the key variables of world petroleum market.…”

USDA Long-Term Meat Trade Projections: A Comprehensive Evaluation

“…There exists a great volume of research evaluating other forecasts and projections produced by USDA, including crop production (Baur and Orazem, 1994;Bora, Katchova, and Kuethe, 2022;Egelkraut et al, 2003;Good, 2006, 2013;Isengildina-Massa, Karali, and Irwin, 2020;Isengildina-Massa, MacDonald, and Xie, 2012;Lewis and Manfredo, 2012), crop prices (Elam and Holder, 1985), grain ending stocks (Xiao, Hart, and Lence, 2017), livestock production (Bailey and Brorsen, 1998;Sanders and Manfredo, 2002), and livestock prices (Kastens, Schroeder, and Plain, 1998;Sanders and Manfredo, 2003). Additionally, there are various works that look at USDA farm economy indicators such as net farm income (Isengildina-Massa et al, 2021;Kuethe et al, 2018) and net cash income (Bora, Katchova, and Kuethe, 2021;Isengildina-Massa et al, 2020;Kuethe, Bora, and Katchova, 2022).…”