Nonlinear regression in tax evasion with uncertainty: a variational approach

Mobasher-Kashani, M., Ayob, M., Bakar, A. A., Tanabandeh, R., Taheri, K. and Tayarani Najaran, M. H. (2017) Nonlinear regression in tax evasion with uncertainty: a variational approach. Pertanika Journal of Science and Technology, 25(S6), pp. 151-162.

Abstract

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Tayarani, Dr Mohammad
• Authors: Mobasher-Kashani, M., Ayob, M., Bakar, A. A., Tanabandeh, R., Taheri, K., and Tayarani Najaran, M. H.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Pertanika Journal of Science and Technology
• Publisher: Universiti Putra Malaysia Press
• ISSN: 0128-7680
• Copyright Holders: Copyright © 2017 Universiti Putra Malaysia Press
• First Published: First published in Pertanika Journal of Science and Technology 25(S6):151-162
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher