A Conditional Value Test of Exogenous Variables in Potato Price Forecasting

Abstract

Whether weather variables improve agricultural price forecasts is usually answered on average, yet theory
and some evidence suggest that weather matters only in particular regimes, such as drought. This study
turns the question into one about conditional value: when does weather help, and can a rule detect those
occasions in real time, Using weekly potato prices with temperature and precipitation (January 2003 –
September 2023), three forecasters are compared in an expanding-window walk-forward design covering
about 15 years of out-of-sample forecasts: a naive no-change forecast (A), a price-only machine-learning
model (B) and a model that adds lagged temperature and precipitation (C). A confidence gate, which opens
only when a model has shown consistent level-accuracy and turning-point improvements over its reference
on recently realised origins, is applied to B versus A and to C versus B, and raw prices are compared with
Kalman-filled prices as inputs. Price-only models clearly beat the naive forecast at four weeks ahead (relative
MAE 0.85-0.90, Diebold-Mariano p < 0.001) but not significantly at one week. Weather did not improve
accuracy on average at either horizon: relative to B, model C ranged from about 0.99 to 1.02 times B’s error
and was significantly worse for ridge regression at four weeks (p = 0.017). The gate between C and B stayed
mostly closed (5-21% of origins), and its open and closed states were no better than chance at identifying
periods in which weather later helped; the gate between B and A cost accuracy relative to always using B,
because B was already reliably better than naive at four weeks. Descriptively, weather added value in the
three drought years in the evaluation sample (MAE ratio 0.97 at one week versus 1.03 in other years), but the
contrast was not statistically significant (p = 0.064). Kalman-filled inputs helped modestly for boosted trees
at one week. The results support a conditional, regime-specific view of weather in these data but show that
detecting the regime from recent forecast performance is hard, so a null result for the gate is informative
about the limits of detecting conditional value.