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A simple selection test between the Gompertz and Logistic growth models

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  • Nguimkeu, Pierre

Abstract

This paper proposes a simple model selection test between the Gompertz and the Logistic growth models based on parameter significance testing in a comprehensive linear regression. Simulations studies are provided to show the accuracy of the method. Two real-data examples are also provided to illustrate the implementation of the proposed method in practice.

Suggested Citation

  • Nguimkeu, Pierre, 2014. "A simple selection test between the Gompertz and Logistic growth models," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 98-105.
    • Handle: RePEc:eee:tefoso:v:88:y:2014:i:c:p:98-105
    DOI: 10.1016/j.techfore.2014.06.017
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    References listed on IDEAS

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    1. Davidson, Russell & MacKinnon, James G, 1981. "Several Tests for Model Specification in the Presence of Alternative Hypotheses," Econometrica, Econometric Society, vol. 49(3), pages 781-793, May.
    2. Chu, Wen-Lin & Wu, Feng-Shang & Kao, Kai-Sheng & Yen, David C., 2009. "Diffusion of mobile telephony: An empirical study in Taiwan," Telecommunications Policy, Elsevier, vol. 33(9), pages 506-520, October.
    3. Nigel Meade, 1988. "A Modified Logistic Model Applied to Human Populations," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 151(3), pages 491-498, May.
    4. Yamakawa, Peter & Rees, Gareth H. & Manuel Salas, José & Alva, Nikolai, 2013. "The diffusion of mobile telephones: An empirical analysis for Peru," Telecommunications Policy, Elsevier, vol. 37(6), pages 594-606.
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    Cited by:

    1. Satoh, Daisuke, 2021. "Discrete Gompertz equation and model selection between Gompertz and logistic models," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1192-1211.
    2. Eryarsoy, Enes & Delen, Dursun & Davazdahemami, Behrooz & Topuz, Kazim, 2021. "A novel diffusion-based model for estimating cases, and fatalities in epidemics: The case of COVID-19," Journal of Business Research, Elsevier, vol. 124(C), pages 163-178.
    3. Sungchul Kim & Dongsik Jang & Sunghae Jun & Sangsung Park, 2015. "A Novel Forecasting Methodology for Sustainable Management of Defense Technology," Sustainability, MDPI, vol. 7(12), pages 1-17, December.

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