Abstract
The nonlinear grey Bernoulli model, abbreviated as NGBM(1,1), has been successfully applied to control, prediction, and decision‐making fields, especially in the prediction of nonlinear small sample time series. However, there are still some problems in improving the prediction accuracy of NGBM(1,1). In this paper, we propose a novel optimized nonlinear grey Bernoulli model for forecasting Chinaʼs GDP. In the new model, the structure and parameters of NGBM(1,1) are optimized simultaneously. Especially, the latest item of first‐order accumulative generating operator (1‐AGO) sequence is taken as the initial condition, then background value is reconstructed by optimizing weights of neighbor values in 1‐AGO sequence, which is based on minimizing the sum of absolute percentage errors, and finally, we establish the new model based on the rolling mechanism. Prediction accuracy of the proposed model is investigated through some simulations and a real example application, and the proposed model is applied to forecast the annual GDP in China from 2019 to 2023.
Highlights
Macroeconomic monitoring, forecasting, and early warning are important branches of economic research and an essential prerequisite for scienti c macroeconomic decisionmaking
To capture the nonlinear trend in annual GDP data from China and obtain an appreciate prediction accuracy, this paper proposes a novel optimized nonlinear grey Bernoulli model, and the main contributions can be summarized as follows: (1) e structure and parameters of the grey prediction model are optimized simultaneously in this paper. e latest item of 1-AGO sequence is taken as the initial condition, and the weighted of neighbor values in background value is determined by minimizing the sum of absolute percentage error
A novel optimized NGBM(1,1) model has been proposed to forecast the annual GDP of China. e numerical results of simulation and real example have implied that the optimized NGBM(1,1) model has more excellent performance on forecasting the annual GDP than the other commonly used models. en, the main conclusions are listed as follows
Summary
Macroeconomic monitoring, forecasting, and early warning are important branches of economic research and an essential prerequisite for scienti c macroeconomic decisionmaking. In the existing NGBM(1,1) model, the initial condition is set to be x(0)(1), the oldest data in the original sequence, this means all of the information expect first item is not fully used for the forecasting model.
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