英文摘要 |
When we analyze the behavior of binary choice, the appropriated econometric methodologies include logistic regression, probit regression and discriminate analysis, and logistic regression is most frequently used. Artificial neural network can also be applied to the binary choice, and back-propagation network (BPN) is most frequently used. We employ logistic regression and back-propagation network to analyze this question, and focus on the predictive performance of the two models. Previous studies used the accuracy rate of prediction as a comparison criterion between logistic regression and back-propagation network. However, the lowest prediction accuracy rate of logistic regression is affected by the sample ratio, we cannot use the accuracy rate to compare the performance of the two different models. In this paper, we will discuss the advantages and disadvantages of logistic regression and BPN, and compare the predictive performance on the same data set. Furthermore, we eliminate some data to get an equal sample ratio data set, and analyze the effect of sample ratio on predictive performance by actual sample ratio and equal sample ratio data set. |