Estimating regional integration using the Bayesian state-space approach - Manuel Leonard Albis; Mara Claire Tayag; Jong Woo Kang (Asian Development Bank (ADB), 2024)
Estimating regional integration faces challenges because of incomplete data from missing values and insufficient time spans. A key advantage of a dynamic factor model estimated using the Bayesian state-space approach is its ability to handle missing values and aggregation of the regional integration indicators. This approach yields estimates of bilateral economic integration (BEI) using regional integration indicators on four dimensions: trade, foreign direct investments, finance, and migration. The regional integration index (RII) is derived by applying network density to the BEI estimates to represent the strength of regional integration within Asia and the Pacific. The BEI indexes not only serve to estimate the overall RII but enable the identification of economy pairs and dimensions that are driving regional integration in Asia and the Pacific. The estimated RII for Asia and the Pacific declined slightly in recent years, and the integration network became more centered around the People's Republic of China.
Keywords: F02; F15; C8; C11; 330; Bayesian state-space model; network density; regional integration index