Comparison of TSCS regression and neural network models for panel data forecasting: Debt policy

Hsiao-Tien Pao*, Yao Yu Chih

*此作品的通信作者

研究成果: Article同行評審

11 引文 斯高帕斯(Scopus)

摘要

Empirical studies of variations in debt ratios across firms have analyzed important determinants of capital structure using statistical models. Researchers, however, rarely employ nonlinear models to examine the determinants and make little effort to identify a superior prediction model among competing ones. This paper reviews the time-series cross-sectional (TSCS) regression and the predictive abilities of neural network (NN) utilizing panel data concerning debt ratio of high-tech industries in Taiwan. We built models with these two methods using the same set of measurements as determinants of debt ratio and compared the forecasting performance of five models, namely, three TSCS regression models and two NN models. Models built with neural network obtained the lowest mean square error and mean absolute error. These results reveal that the relationships between debt ratio and determinants are nonlinear and that NNs are more competent in modeling and forecasting the test panel data. We conclude that NN models can be used to solve panel data analysis and forecasting problems.

原文English
頁(從 - 到)117-123
頁數7
期刊Neural Computing and Applications
15
發行號2
DOIs
出版狀態Published - 1 4月 2006

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