Title: Non-parametric combination forecasting methods with application to GDP forecasting

Authors: Wei Li; Yunyan Wang

Addresses: School of Science, Jiangxi University of Science and Technology, No. 86, Hongqi Ave., Ganzhou, 341000, Jiangxi, China ' School of Science, Jiangxi University of Science and Technology, No. 86, Hongqi Ave., Ganzhou, 341000, Jiangxi, China

Abstract: This work is devoted to constructing non-parametric combination prediction methods, which can improve the forecasting effect and accuracy to some extent. In this paper, in order to forecast the regional gross domestic product, a non-parametric autoregressive method is introduced into the autoregressive integrated moving average model, and a combined method of ARIMA model and non-parametric autoregressive model is established based on the residual correction. Furthermore, the specific prediction steps are proposed. The empirical results show that the new proposed combined model outperforms both the ARIMA model and the non-parametric autoregressive model in terms of regression effect and forecasting accuracy. The combination of parametric model and non-parametric model not only provides a method with better applicability and prediction effect for the establishment of GDP prediction model, but also provides a theoretical basis for the prediction of relevant economic data in the future. The prediction results show that during the China's 14th Five-Year Plan period, the gross domestic product of Jiangxi Province will increase by 7.01% annually.

Keywords: gross domestic product; GDP; ARIMA model; non-parametric autoregressive model; residual correction; combined model.

DOI: 10.1504/IJCSE.2023.135278

International Journal of Computational Science and Engineering, 2023 Vol.26 No.6, pp.694 - 701

Received: 23 Dec 2021
Received in revised form: 16 Apr 2022
Accepted: 10 May 2022
Published online: 04 Dec 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article