An Evaluation of the Rice Outlook and Situation Price Forecasts

Abstract

The Rice Outlook and Situation (RO&S) forecasts were compared to the forecasts of a univariate Box-Jenkins (BJ) model. On balance, the RO&S forecasts had lower mean square forecast errors and lower mean absolute forecast errors than the BJ model forecasts. The differences in the squared and absolute forecast errors were not significant, however. Based on the concept of conditional efficiency as set forth by Granger and Newbold, it was found that the BJ forecasts did not add any information that might improve forecast accuracy beyond what was already incorporated in the RO&S forecasts.

A Sequential Rationality Test of USDA Preliminary Price Estimates for Selected Program Crops: Rice, Soybeans, and Wheat

“…For rice, the realized price is consistently higher by nearly 9.6% than the preliminary estimates. Clearly, the USDA estimate for rice is neither unbiased nor optimal in the long run, as opposed to the early findings of Elam and Holder (1985). Table 4 also shows that there is no heteroskedasticity in residuals at conventional significance levels.…”

“…Optimality testing was carried out to examine whether forecasts are properly scaled for realized values (Patton and Timmermann 2007). Unbiasedness testing was performed to examine whether forecasts are downward or upward biased with respect to mean actual values (Elam and Holder 1985). Consistency testing was implemented to determine whether the actual values and forecasts are drifted away from each other (Cheung and Chinn 1998).…”

“…Numerous researchers have scrutinized USDA price forecasts in terms of absolute accuracy (Elam and Holder 1985;Kastens et al 1998), bias and efficiency (Sanders and Manfredo 1998), or directional accuracy (No 2007). Although the researchers above have used different forecast evaluation methodologies, they have implemented a simple strategy that N step ahead of the USDA forecast, E(P t+n |Ω t ) is compared with their own forecast, E(P t+n |X t ), where Ω t is a quantitative and qualitative information set available at the USDA and at time t (Vogel and Bange 1999, p.10) and X t is a vector of price related time series as well as actual price available for researchers at time t. Based on various accuracy criteria, their reports on the performance of USDA price forecasts are diverse.…”

“…For instance, Elam and Holder (1985) found that USDA rice forecasts have lower mean square forecast errors than random walk model forecasts. Sanders and Manfredo (1998) found that USDA forecasts are statistically more accurate than competing times series forecasts for fluid milk.…”

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).…”