Some asymptotic results of a non‐parametric conditional mode estimator for functional time‐series data

We consider the estimation of the conditional mode function when the covariates take values in some abstract function space. The main goal of this paper was to establish the almost complete convergence and the asymptotic normality of the kernel estimator of the conditional mode when the process is assumed to be strongly mixing and under the concentration property over the functional regressors. Some applications are given. This approach can be applied in time‐series analysis to the prediction and confidence band building. We illustrate our methodology by using El Nio data.     

Modeling producer responses with dynamic programming: a case for adaptive crop management

Past research found agricultural producers’ conditional responses during the growing season are important adaptations to weather and other stochastic events. Failing to recognize these responses overstates the risks confronting producers and understates their ability to respond to adverse circumstances. Dynamic programming (DP) provides a means for determining optimal long‐term crop management plans. However, most applications in the literature base their analysis on annual time steps with fixed strategies within the year, effectively ignoring conditional responses during the year. We suggest an alternative approach that captures the strategic responses within a cropping season to random weather variables as they unfold, reflecting farmers’ ability to adapt to weather realizations. We illustrate our approach by applying it to a typical cereal farm in Karak, Jordan. The results show that including conditional within‐year responses to weather reduces the frequency of fallowing by 23% and increases expected income by 9%.