Nonparametric Kernel Regression Estimation in Determinants of Religious Giving
Date Issued
2016
Date
2016
Author(s)
Lien, Ya-Ting
Abstract
The parametric estimation method used to make several assumptions on the population and data. In real case, however, researchers often have to ignore these violations. In non-parametric methods, researchers don’t have to make so many assumptions as they do in parametric estimation. In addition, using non-parametric methods, researchers can get a better fitted model for the data. The application of non-parametric methods in religious giving studies is quite rare, therefore in this study, we introduced the non-parametric kernel regression method to estimate the 2013~2014 religious giving amount of Taiwan. We compared the results of multiple linear regression, Tobit regression and non-parametric kernel regression and found that the kernel regression model shows the best fitting and the smallest RSE. Also, the significance of each coefficients in kernel regression is quite different from that in multiple regression and Tobit regression.
Subjects
NONPARAMETRIC REGRESSION
KERNEL
RELIGIOUS BEHAVIOR
NARADAYA-WATSON
Type
thesis
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