Home / Regular Issue / JSSH Vol. 25 (S) Jun. 2017 / JST-S0385-2017


Nonlinear Regression in Tax Evasion with Uncertainty: a Variational Approach

Mohamad Mobasher-Kashani, Masri Ayob, Azuraliza Abu Bakar, Razieh Tanabandeh, Kourosh Taheri and Mohammad Hassan Tayarani Najaran

Pertanika Journal of Social Science and Humanities, Volume 25, Issue S, June 2017

Keywords: Bayesian inference, Linear regression, Nonlinear problem, Tax evasion, Uncertainty, Variational approximation

Published on: 12 Mac 2018

One of the major problems in today's economy is the phenomenon of tax evasion. The linear regression method is a solution to find a formula to investigate the effect of each variable in the final tax evasion rate. Since the tax evasion data in this study has a great degree of uncertainty and the relationship between variables is nonlinear, Bayesian method is used to address the uncertainty along with 6 nonlinear basis functions to tackle the nonlinearity problem. Furthermore, variational method is applied on Bayesian linear regression in tax evasion data to approximate the model evidence in Bayesian method. The dataset is collected from tax evasion in Malaysia in period from 1963 to 2013 with 8 input variables. Results from variational method are compared with Maximum Likelihood Estimation technique on Bayeisan linear regression and variational method provides more accurate prediction. This study suggests that, in order to reduce the tax evasion, Malaysian government should decrease direct tax and taxpayer income and increase indirect tax and government regulation variables by 5% in the small amount of changes (10%-30%) and reduce direct tax and income on taxpayer and increment indirect tax and government regulation variables by 90% in the large amount of changes (70%-90%) with respect to the current situation to reduce the final tax evasion rate.

ISSN 0128-7702

e-ISSN 2231-8534

Article ID


Download Full Article PDF

Share this article

Recent Articles