Dynamic Process Models for the Evaluation of the Compliance Level Evolution: Evidence from Italy

Abstract

Decision support systems are used in different contexts, including tax policies. In these pages a theoretical analysis is proposed, based on random utility maximization and dynamic process models, to forecast the evolution of the taxable income produced by the application of a rewarding-based compliance methodology recently introduced in Italy. We will briefly describe the econometric characteristics of the new fiscal methodology (SIR); then, we will focus on the theoretical aspects concerning the evolution of the taxable income, in case of both a stochastic and a deterministic process, the latter commonly used to estimate a limit condition of the process (usually a worst-case scenario). The results here obtained can be extended to different kind of tax compliance models and are supposed to be as a basic step to implement an effective IT decision support model. The theoretical connection between SIR and sector studies, the previous compliance methodology, will be discussed. Finally, a numerical example of a deterministic process model based on synthetic data will be provided.