Multi-process-based maximum entropy bootstrapping estimator: Application for net foreign direct investment in ASEAN

Abstract

Due to a broad consensus in the engaging of global economic integrations, host countries encounter a number of challenges, especially in international capital mobility. Foreign direct investment (FDI) becomes a pillar for economic development. This study explores which Association of Southeast Asian Nations (ASEAN)-6 countries are good representatives to inform the directions of FDI. For computational modelling, the AR-GARCH model was created using the maximum entropy bootstrap estimation. Nonparametric techniques consisting of the maximum entropy bootstrap method and cross-entropy algorithm were applied. The results show that Indonesia has the nearest cross-entropy (CE) value compared to the whole entropy value, followed by Thailand and Singapore. Furthermore, it is consistent with the first- and second-order stochastic dominance analyses. Additionally, the structural dependence of capital movements is displayed to deeply investigate the capital flow relation among the countries. Consequently, the performances of FDI in Indonesia, Thailand, and Singapore can significantly convey the scenario of FDI across ASEAN.