Symbolic classification methods applied to the intervals of quantile estimates of production costs

Overview & Synopsis

This presentation uses symbolic data classification to explore the similarities between distributions of conditional quantile estimates, applying it to the problem of specific cost allocation in agriculture. After recalling the conceptual framework of agricultural production cost estimation, the first part presents the empirical model, the quantile regression approach and the interval data classification technique used. The second part presents the comparative analysis between twelve European Member States of the results of the hierarchical divisive classification of estimation intervals, applied to the estimation of fertiliser costs.