Nonlinear regression in tax evasion with uncertainty

A variational approach

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

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)151-162
Number of pages12
JournalPertanika Journal of Science and Technology
Volume25
Issue numberS6
Publication statusPublished - 1 Jun 2017

Fingerprint

Taxes
taxes
Taxation
Uncertainty
uncertainty
Income Tax
Government Regulation
Linear Models
Bayes Theorem
Linear regression
Bayesian theory
laws and regulations
Malaysia
income
tax
methodology
Maximum likelihood estimation
nonlinearity
method

Keywords

  • Bayesian inference
  • Linear regression
  • Nonlinear problem
  • Tax evasion
  • Uncertainty
  • Variational approximation

ASJC Scopus subject areas

  • Computer Science(all)
  • Chemical Engineering(all)
  • Environmental Science(all)
  • Agricultural and Biological Sciences(all)

Cite this

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

Nonlinear regression in tax evasion with uncertainty : A variational approach. / Mobasher-Kashani, Mohamad; Ayob, Masri; Abu Bakar, Azuraliza; Tanabandeh, Razieh; Taheri, Kourosh; Najaran, Mohammad Hassan Tayarani.

In: Pertanika Journal of Science and Technology, Vol. 25, No. S6, 01.06.2017, p. 151-162.

Research output: Contribution to journalArticle

Mobasher-Kashani, M, Ayob, M, Abu Bakar, A, Tanabandeh, R, Taheri, K & Najaran, MHT 2017, 'Nonlinear regression in tax evasion with uncertainty: A variational approach', Pertanika Journal of Science and Technology, vol. 25, no. S6, pp. 151-162.
Mobasher-Kashani, Mohamad ; Ayob, Masri ; Abu Bakar, Azuraliza ; Tanabandeh, Razieh ; Taheri, Kourosh ; Najaran, Mohammad Hassan Tayarani. / Nonlinear regression in tax evasion with uncertainty : A variational approach. In: Pertanika Journal of Science and Technology. 2017 ; Vol. 25, No. S6. pp. 151-162.
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