A Decision Support System for Corporate Tax Arrears Prediction
Õie Renata Siimon () and
Oliver Lukason ()
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Õie Renata Siimon: School of Economics and Business Administration, University of Tartu, 51009 Tartu, Estonia
Oliver Lukason: School of Economics and Business Administration, University of Tartu, 51009 Tartu, Estonia
Sustainability, 2021, vol. 13, issue 15, 1-23
This paper proposes a decision support system to predict corporate tax arrears by using tax arrears in the preceding 12 months. Despite the economic importance of ensuring tax compliance, studies on predicting corporate tax arrears have so far been scarce and with modest accuracies. Four machine learning methods (decision tree, random forest, k-nearest neighbors and multilayer perceptron) were used for building models with monthly tax arrears and different variables constructed from them. Data consisted of tax arrears of all Estonian SMEs from 2011 to 2018, totaling over two million firm-month observations. The best performing decision support system, yielding 95.3% accuracy, was a hybrid based on the random forest method for observations with previous tax arrears in at least two months and a logical rule for the rest of the observations.
Keywords: tax arrears; SMEs; time series classification; machine learning; predictive models (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:15:p:8363-:d:602207
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