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Comparison of Linear and Nonlinear Models for Panel Data Forecasting: Debt Policy in Taiwan

Author

Listed:
  • Hsiao-Tien Pao

    (Department of Management Science, National Chiao Tung University, Hsinchu, Taiwan, ROC)

  • Yao-Yu Chih

    (Department of Management Science, National Chiao Tung University, Hsinchu, Taiwan, ROC)

Abstract

This paper discusses the time-series cross-sectional (TSCS) regression and the prediction ability of the artificial neural network (ANN) by examining the panel data of debt ratios of the high tech industry in Taiwan. We build models with these two methods and eight determinants of debt ratio and compare the forecast performances of five models, two ANN nonlinear models and three traditional TSCS linear models. The results show that the sign of each determinant in linear models is the same as that in ANN models. In addition, the insignificant determinants in linear models have low relative sensitivities in ANN models. It seems that these two methods show consistent results for the capital structure determinants. Researchers and practitioners can employ either ANN or traditional statistical model to analyze the important determinants of the capital structure of their firms. The results of comparing the out-of-sample forecasting capabilities of the two methods indicate that: (1) the proposed ANN with 1-year lag model shows better forecast performance than the other three linear models in spite of high or low debt ratio; (2) the debt ratios of the present year are highly related to those of the previous year; and (3) the ANN model is capable of catching sophisticated nonlinear integration effects. Consequently, the ANN method is the more appropriate one between the two methods to be applied to build a forecasting model for the high tech industry in Taiwan.

Suggested Citation

  • Hsiao-Tien Pao & Yao-Yu Chih, 2005. "Comparison of Linear and Nonlinear Models for Panel Data Forecasting: Debt Policy in Taiwan," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 8(03), pages 525-541.
    • Handle: RePEc:wsi:rpbfmp:v:08:y:2005:i:03:n:s0219091505000488
    DOI: 10.1142/S0219091505000488
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    Citations

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    Cited by:

    1. Ying-Fang Huang & Chia-Nan Wang & Hoang-Sa Dang & Shun-Te Lai, 2015. "Predicting the Trend of Taiwan’s Electronic Paper Industry by an Effective Combined Grey Model," Sustainability, MDPI, vol. 7(8), pages 1-20, August.
    2. Ralf Sabiwalsky, 2008. "Nonlinear Modeling of Target Leverage with Latent Determinant Variables – New Evidence on the Trade-off Theory," SFB 649 Discussion Papers SFB649DP2008-062, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    3. Robert Freeman & Adam Koch & Haidan Li, 2011. "Can historical returns-earnings relations predict price responses to earnings news?," Review of Quantitative Finance and Accounting, Springer, vol. 37(1), pages 35-62, July.
    4. Tarek Eldomiaty & Marina Apaydin & Mona Yusuf & Mohamed Rashwan, 2023. "How Do Stock Market Development and Competitiveness Affect Equity Risk Premium? Implications from World Economies," IJFS, MDPI, vol. 11(1), pages 1-19, February.
    5. David Newton, 2019. "Are All Forecasts Made Equal? Conditioning Models on Fit to Improve Accuracy," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-32, September.
    6. Sabiwalsky, Ralf, 2010. "Nonlinear modelling of target leverage with latent determinant variables -- new evidence on the trade-off theory," Review of Financial Economics, Elsevier, vol. 19(4), pages 137-150, October.
    7. Ralf Sabiwalsky, 2010. "Nonlinear modelling of target leverage with latent determinant variables — new evidence on the trade‐off theory," Review of Financial Economics, John Wiley & Sons, vol. 19(4), pages 137-150, October.

    More about this item

    Keywords

    Artificial neural networks; TSCS regression; forecasting; capital structure; panel data;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G2 - Financial Economics - - Financial Institutions and Services
    • G3 - Financial Economics - - Corporate Finance and Governance

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