FORECASTING GROSS DOMESTIC PRODUCT (GDP) OF OECD COUNTRIES BASED ON DIGITAL TRANSFORMATION INDICATORS: ANN APPROACH

REFERENCES

• Acemoglu, D., Laibson, D., & List, J. (2018). Economics (2ª edición). Pearson education limited.
   Google Scholar
• Altin, M., & Tasdemir, S. (2016). Application of ANN modelling of fire door resistance. International Journal of Intelligent Systems and Applications in Engineering, 4(2), 45–48. https://doi.org/10.18201/ijisae.90445
   Google Scholar
• Arvidsson, A. (2019). Changemakers: The industrious future of the digital economy. John Wiley & Sons.
   Google Scholar
• Barsoum, F., & Stankiewicz, S. (2015). Forecasting GDP growth using mixed-frequency models with switching regimes. International Journal of Forecasting, 31(1), 33–50. https://doi.org/10.1016/j.ijforecast.2014.04.002
   Web of Science ®Google Scholar
• Blanchard, O., Amighini, A., & Giavazzi, F. (2021). Macroeconomics. A European perspective. Pearson.
   Google Scholar
• Dang n’Guyen, G. (2019). Companies: The great transformation. The Digital Era 2: Political Economy Revisited, 7–22.
   Google Scholar
• Eftimoski, D. (2019). Improving short-term forecasting of Macedonian GDP: Comparing the factor model with the macroeconomic structural equation model. Romanian Journal of Economic Forecasting, 22(2), 32.
   Web of Science ®Google Scholar
• EUI. (2020). https://theinclusiveinternet.eiu.com/explore/countries/performance
   Google Scholar
• Geman, S., Bienenstock, E., & Doursat, R. (1992). Neural networks and the bias/variance dilemma. Neural Computation, 4(1), 1–58. https://doi.org/10.1162/neco.1992.4.1.1
   Web of Science ®Google Scholar
• Hlaváček, M., Čada, J., & Hakl, F. (2005). The application of structured feedforward neural networks to the modelling of the daily series of currency in circulation [Paper presentation]. International Conference on Natural Computation, Berlın.
   Google Scholar
• Illing, G., & Peitz, M. (2006). Industrial organization and the digital economy. Mit Press.
   Google Scholar
• Jemli, R., Chtourou, N., & Feki, R. (2010). Insurability challenges under uncertainty: An attempt to use the artificial neural network for the prediction of losses from natural disasters. Panoeconomicus, 57(1), 43–60. https://doi.org/10.2298/PAN1001043J
   Web of Science ®Google Scholar
• Jena, P. R., Majhi, R., & Majhi, B. (2015). Development and performance evaluation of a novel Knowledge Guided Artificial Neural Network (KGANN) model for exchange rate prediction. Journal of King Saud University-Computer & Information Sciences, 27(4), 450–457. https://doi.org/10.1016/j.jksuci.2015.01.002
   Google Scholar
• Kewsuwun, N. (2020). The digital economy: Rethinking promise and peril in the age of networked intelligence (2nd). Journal of Information Science, 38(2), 84–92.
   Google Scholar
• Kordanuli, B., Barjaktarović, L., Jeremić, L., & Alizamir, M. (2017). Appraisal of artificial neural network for forecasting of economic parameters. Physica A: Statistical Mechanics and Its Applications, 465, 515–519. https://doi.org/10.1016/j.physa.2016.08.062
   Web of Science ®Google Scholar
• Kotorov, R. (2020). Data-driven business models for the digital economy. Business Expert Press.
   Google Scholar
• Milačić, L., Jović, S., Vujović, T., & Miljković, J. (2017). Application of artificial neural network with extreme learning machine for economic growth estimation. Physica A: Statistical Mechanics and Its Applications, 465, 285–288. https://doi.org/10.1016/j.physa.2016.08.040
   Web of Science ®Google Scholar
• Ozkan, I. A., Koklu, M., & Sert, I. U. (2018). Diagnosis of urinary tract infection based on artificial intelligence methods. Computer Methods and Programs in Biomedicine, 166, 51–59. https://doi.org/10.1016/j.cmpb.2018.10.007
   PubMed Web of Science ®Google Scholar
• Picot, A., Florio, M., Grove, N., & Kranz, J. (2016). The economics of infrastructure provisioning: The changing role of the state. MIT press.
   Google Scholar
• Ramos-Pérez, E., Alonso-González, P. J., & Núñez-Velázquez, J. J. (2019). Forecasting volatility with a stacked model based on a hybridized artificial neural network. Expert Systems with Applications, 129, 1–9. https://doi.org/10.1016/j.eswa.2019.03.046
   Web of Science ®Google Scholar
• Shaw, M. J. (2015). E-commerce and the digital economy. Routledge.
   Google Scholar
• Sokolov-Mladenović, S., Milovančević, M., Mladenović, I., & Alizamir, M. (2016). Economic growth forecasting by artificial neural network with extreme learning machine based on trade, import and export parameters. Computers in Human Behavior, 65, 43–45. https://doi.org/10.1016/j.chb.2016.08.014
   Web of Science ®Google Scholar
• Sudoh, O., & Sudoh, O. (2005). Digital economy and social design. Springer.
   Google Scholar
• Tassey, G. (1982). Infratechnologies and the role of government. Technological Forecasting and Social Change, 21(2), 163–180. https://doi.org/10.1016/0040-1625(82)90013-0
   Web of Science ®Google Scholar
• Tümer, A. E., & Akkus, A. (2019). Application of radial basis function networks with feature selection for gdp per capita estimation based on academic parameters. Computer Systems Science and Engineering, 34(3), 145–150. https://doi.org/10.32604/csse.2019.34.145
   Web of Science ®Google Scholar
• Tümer, A. E., & Akkuş, A. (2018). Forecasting gross domestic product per capita using artificial neural networks with non-economical parameters. Physica A: Statistical Mechanics and Its Applications, 512, 468–473. https://doi.org/10.1016/j.physa.2018.08.047
   Web of Science ®Google Scholar
• Union, T. (2020). International Telecommunication Union. Statistics.
   Google Scholar
• Watanabe, C., Naveed, K., Tou, Y., & Neittaanmäki, P. (2018). Measuring GDP in the digital economy: Increasing dependence on uncaptured GDP. Technological Forecasting and Social Change, 137, 226–240. https://doi.org/10.1016/j.techfore.2018.07.053
   Web of Science ®Google Scholar
• Xia, S., He, J., & Chu, H. (2011, May 29–June 1). The study on models adjustment and generation capability of artificial neural network [Paper presentation]. Advances in Neural Networks–ISNN 2011: 8th International Symposium on Neural Networks, ISNN 2011, Guilin, China.
   Google Scholar
• Yang, X., Zhang, Z., Cuthbert, L., & Wang, Y. (2019). Forecasting macao GDP using different artificial neural networks [Paper presentation]. Information Science and Applications 2018: ICISA 2018, Hong Kong.
   Google Scholar
• Zekos, G. I. (2020). Economics and law of digital economy. Novinka.
   Google Scholar
• Zhang, M. L., & Chen, M. S. (2019). China’s digital economy: Opportunities and risks. International Monetary Fund.
   Google Scholar