The aim of this study is to explore the impact between religiosity and voluntary tax compliance and enforced tax compliance for self-employed taxpayers in Turkey, where Islam is the predominant religion. A questionnaire survey was administrated to 375 male and 28 female self-employed taxpayers. In this paper, two dimensions of religiosity, namely interpersonal and intrapersonal religiosity, were studied. Factor analysis and ordinary least squares regression methods were used for data analyses. The results of the study illustrate that general religiosity has a statistically positive impact on both voluntary and enforced tax compliance. When we consider the dimensions of religiosity, only intrapersonal religiosity appears to be a significant contributor only to voluntary tax compliance. Nevertheless, interpersonal religiosity has no significant statistical effect on both voluntary and enforced tax compliance. This is one of the pioneer studies of its kind, and investigates the relationship between religiosity and tax compliance from the perspective of developing countries, particularly, Turkey.

We propose a re-scaled LASSO by pre-multiplying the LASSO with a matrix term, namely, scaled LASSO (SLASSO), for multicollinear situations. Our numerical study has shown that the SLASSO is comparable with other sparse modeling techniques and often outperforms the LASSO and elastic net. Our findings open new visions about using the LASSO still for sparse modeling and variable selection. We conclude our study by pointing that the same efficient algorithm can solve the SLASSO for solving the LASSO and suggest following the same construction technique for other penalized estimators

In this paper, we consider estimation techniques based on ridge regression when the matrix ${\mathbf{X}}^{\mathrm{\top }}\mathbf{X}$ appears to be ill-conditioned in the partially linear model using kernel smoothing. Furthermore, we consider that the coefficients $\mathit{\beta }$ can be partitioned as $({\mathit{\beta }}_1,{\mathit{\beta }}_2)$ where ${\mathit{\beta }}_1$ is the coefficient vector for main effects, and ${\mathit{\beta }}_2$ is the vector for ‘nuisance’ effects. We are essentially interested in the estimation of ${\mathit{\beta }}_1$ when it is reasonable to suspect that ${\mathit{\beta }}_2$ is close to zero. We suggest ridge pretest, ridge shrinkage and ridge positive shrinkage estimators for the above semi-parametric model, and compare its performance with some penalty estimators. In particular, suitability of estimating the nonparametric component based on the kernel smoothing basis function is also explored. Monte Carlo simulation study is used to compare the relative efficiency of proposed estimators, and a real data example is presented to illustrate the usefulness of the suggested methods. Moreover, the asymptotic properties of the proposed estimators are obtained.

This study presents the results of an empirical study of ethical attitudes toward bribe taking in six religions—Christianity, Islam, Buddhism, the Baha’i faith, Hinduism, and Judaism. The paper begins with a discussion of the theoretical and empirical literature on the subject. The empirical part of the study examines attitudes toward accepting bribes in 57 countries from the perspectives of six religions using the data from Wave 6 (2010–2014) of the World Values Survey. The sample population is more than 52,000. More than a dozen demographic variables were examined. The study found that attitude toward bribe taking does differ by religion.