Customs Revenues Prediction Using Ensemble Methods (Statistical Modelling vs Machine Learning)

Abstract

This paper considers the problems associated with prediction of customs revenues by ministries of finance and customs administrations. Accurate predictions of customs revenues result in better liquidity of the central budget, and for that reason, they are extremely important for successful management of public finances. The orthodox approach to forecasting revenues is usually based on forecasting revenues based on tax buoyancy and tax elasticity, with respect of some economic proxy. However, this approach has some shortcomings which can negatively affect accuracy, and for that reason we examine different approaches (machine learning and ensembling). Namely, nowadays in the era of Big Data and digitisation in customs, new approaches based on computer algorithms can give us better results as compared to classic modelling. The paper concludes that using ensemble methods that combine different types of heterogeneous models such as statistical modelling and machine learning can improve forecast accuracy when predicting customs revenues.