An Alternative Method to Forecast Season-Average Price for U.S. Corn

Etienne, Xiaoli L.; Farhangdoost, Sara; Hoffman, Linwood A.

doi:10.2139/ssrn.3306590

Cited by 2 publications

(2 citation statements)

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“…We judge sharpness and calibration of each approach according to the CRPS. Forecasts that produce lower CRPS values are preferred; following Colino et al (2012) and Etienne, Farhangdoost, and Hoffman (2019), we compare the average scores produced by models at each forecast step using modified Diebold-Mariano tests (Harvey, Leybourne, and Newbold 1997). To further evaluate the sample-wide calibration of forecast models, we explore their coverage at several selected confidence levels, that is, whether the model-predicted level of uncertainty at forecast time matched the realized uncertainty over the period of observation; and like Isengildina-Massa and Sharp ( 2012), we assess their statistical equivalence using unconditional coverage tests (Christoffersen 1998).…”

Section: Generating Density Forecasts For Agricultural Commoditiesmentioning

confidence: 99%

Incorporating Uncertainty into USDA Commodity Price Forecasts

Adjemian

Bruno

Robe

2020

American J Agri Economics

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From 1977 through April 2019, USDA published monthly season‐average price (SAP) forecasts for key agricultural commodities in the form of intervals meant to indicate forecasters' uncertainty but without attaching a confidence level. In May 2019, USDA eliminated the intervals and began publishing a single point estimate—a value that has a very low probability of being realized. We demonstrate how a density forecasting format can improve the usefulness of USDA price forecasts and explain how such a methodology can be implemented. We simulate 21 years of out‐of‐sample density‐based SAP forecasts using historical data, with forward‐looking, backward‐looking, and composite methods, and we evaluate them based on commonly‐accepted criteria. Each of these approaches would offer USDA the ability to portray richer and more accurate price forecasts than its old intervals or its current single point estimates. Backward‐looking methods require little data and provide significant improvements. For commodities with active derivatives markets, option‐implied volatilities (IVs) can be used to generate forward‐looking and composite models that reflect (and adjust dynamically to) market sentiment about uncertainty—a feature that is not possible using backward‐looking data alone. At certain forecast steps, a composite method that combines forward‐ and backward‐looking information provides useful information regarding farm‐level prices beyond that contained in IVs.

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Section: Generating Density Forecasts For Agricultural Commoditiesmentioning

confidence: 99%

Incorporating Uncertainty into USDA Commodity Price Forecasts

Adjemian

Bruno

Robe

2020

American J Agri Economics

View full text Add to dashboard Cite

show abstract

“…We judge sharpness and calibration of each approach according to the CRPS. Forecasts that produce lower CRPS values are preferred; following Colino et al (2012) and Etienne, Farhangdoost, and Hoffman (2019), we compare the average scores produced by models at each forecast step using modified Diebold-Mariano tests (Harvey, Leybourne, and Newbold 1997). To further evaluate the sample-wide calibration of forecast models, we explore their coverage at several selected confidence levels, that is, whether the model-predicted level of uncertainty at forecast time matched the realized uncertainty over the period of observation; and like Isengildina-Massa and Sharp (2012), we assess their statistical equivalence using unconditional coverage tests (Christoffersen 1998).…”

Section: Comparing the Backward-looking Forward-looking And Composimentioning

confidence: 99%