Publisher
Springer Nature Switzerland
Reference16 articles.
1. Sun, Y., Hong, Y., Wang, S.: Out-of-sample forecasts of China’s economic growth and inflation using rolling weighted least squares. J. Manage. Sci. Eng. 4(1), 1–11 (2019)
2. Falk, B., Roy, A.: Forecasting using the trend model with autoregressive errors. Int. J. Forecasting 21(2), 291–302 (2005)
3. Rungrapee, P., Yamaka, W.: Why the use of convex combinations works well for interval data: a theoretical explanation. Int. J. Uncertainty Fuzziness Knowl.-Based Syst. 28(Supp01), 81–85
4. Wabomba, M.S., Mutwiri, M.M.P., Fredrick, M.: Modeling and forecasting Kenyan GDP using autoregressive integrated moving average (ARIMA) models. Sci. J. Appl. Math. Stat. 4(2), 64–73 (2016)
5. Longo, L., Riccaboni, M., Rungi, A.: A neural network ensemble approach for GDP forecasting. J. Econ. Dyn. Control 134, 104278 (2022)