Hybrid model based on genetic algorithms and neural networks to forecast tax collection: Application using endogenous and exogenous variables

Abstract

Everywhere in the world tax revenues are rolled back for the commonwealth to invest and finance goods and public services, such as: infrastructure, health, security and education. The predict income revenue (taxes) is one of the challenges that the Secretariat of the Federal Revenue of Brazil (RFB for its Portuguese acronym) has. This is an important challenge since the obtained information is valuable to support the decisions pertaining the federal government financial planning. In this work, it is introduced a hybrid model based on Genetic Algorithms (GAs) and Neural Networks (NNs) for a multi-step forecast of tax revenue collection. The results were more accurate in comparison to the outcome the RFB had estimated with the indicators method. The forecast results using endogenous and exogenous variables were divided into two parts: (i) in 2013 (validation period), there was obtained a Mean Absolute Percentage Error (MAPE) of 2.37% and a decrease of the Relative Error of 11.38% to 0.49%; (ii) in 2014 (testing data set) a decrease of Relative Error of 10.82% to 3.51% was obtained.