Analyzing China's OFDI using a novel multivariate grey prediction model with Fourier series. Issue 3 (12th August 2019)

• Record Type: Journal Article
• Authors: Jiang, Hang; Hu, Yi-Chung; Lin, Jan-Yan; Jiang, Peng
• Is Part Of: International journal of intelligent computing and cybernetics. Volume 12:Issue 3(2019)
• Page Start: 352
• Page End: 371
• Publication Date: 2019-08-12
• Subjects: OFDI -- Fourier series -- Grey prediction -- Residual modification -- GM(1, N); Artificial intelligence -- Periodicals; Cybernetics -- Periodicals; 006.3
• DOI: 10.1108/IJICC-07-2018-0095
• Languages: English
• ISSNs: 1756-378X

Abstract

Abstract : Purpose: With the development of economy, China's OFDI constantly increase in recent year. Meanwhile, OFDI has spillover effect on economic development and technological development of home country. Thus, accurate OFDI prediction is a prerequisite for the effective development of international investment strategies. The purpose of this paper is to predict China's OFDI accurately using a novel multivariable grey prediction model with Fourier series. Design/methodology/approach: This paper applied a multivariable grey prediction model, GM(1, N), to forecast China's OFDI. In order to improve the prediction accuracy and without changing local characteristics of grey model prediction, this paper proposed a novel grey prediction model to improve the performance of the traditional GM(1, N) model by combining with residual modification model using GM(1, 1) model and Fourier series. Findings: The coefficients indicate that the export and GDP have positive influence on China's OFDI, and, according to the prediction result, China's OFDI shows a growing trend in next five years. Originality/value: This paper proposed an effective multivariable grey prediction model that combined the traditional GM(1, N) model with a residual modification model in order to predict China's OFDI. Accurate forecasting of OFDI provides reference for the Chinese Government to implement international investment strategies.