2005
DOI: 10.1007/s00521-005-0014-x
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Comparison of TSCS regression and neural network models for panel data forecasting: debt policy

Abstract: 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 bui… Show more

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Cited by 11 publications
(3 citation statements)
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“…A unique study by Pao and Chih [22] concluded that ANN models can be used to solve panel data regression, and that would allow us to construct and test sophisticated models than purely cross-sectional or time-series data to solve debt policy forecasting problems.…”
Section: As An Alternative To Computational Intelligence Techniques mentioning
confidence: 99%
“…A unique study by Pao and Chih [22] concluded that ANN models can be used to solve panel data regression, and that would allow us to construct and test sophisticated models than purely cross-sectional or time-series data to solve debt policy forecasting problems.…”
Section: As An Alternative To Computational Intelligence Techniques mentioning
confidence: 99%
“…One of the first works in the literature to use of neural networks for the prediction of economic data was Pao (2006) who concluded that neural networks had a capability equivalent or superior to that of traditional regression techniques for the prediction of time series in economics. In (Kuhlman, 2017) a group lasso predictive model was trained to generate an index developed by the authors themselves that aims to measure the level of innovation in each country.…”
Section: Resultsmentioning
confidence: 99%
“…This has resulted in applications in many domains, including chemistry [1], economics [17] and hydrology [3]. ANNs have also seen successful use in electro-magnetism and microwave applications.…”
Section: Introductionmentioning
confidence: 99%