Determinants of Regional Raw Milk Prices in Russia

Abstract

Drivers of regional milk price differences across Russian regions are difficult to determine due to limited data availability and restrictions on data collection. In this study, official data from Russian regions for the period from 2013 to 2018 was analysed based on 18 predictor variables in order to explain the regional raw milk price. Due to various data-based restrictions, the use of conventional panel regression models was limited and the analysis was therefore performed based on a Random Forest (RF) machine learning algorithm. Model training and hyperparameter optimization was performed on the training data set with time folds cross-validation. The findings of the study showed that the RF algorithm has a good predictive performance in the test data set even with the default RF values. Finally, the RF variable importance showed that income, gross regional product, livestock density, and milk yield are the four most important variables for explaining the variation in regional milk prices