เปรียบเทียบวิธี

	ผลกระทบแบบตรึงเชิงโต้ตอบ	แบบจำลอง Global VAR	แบบจำลอง VAR ที่เสริมด้วยปัจจัยแปรผันตามเวลา
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model	Regression model
ปีกำเนิด	2009	2004	2005
ผู้ริเริ่ม	Jushan Bai	Pesaran, Schuermann, and Weiner	Bernanke, Boivin, and Eliasz
ประเภท	Panel with latent structure	International system model	Time-varying system
แหล่งต้นตำรับ	Bai, J. (2009). Panel data models with interactive fixed effects. Econometric Reviews, 28(4), 289-312.	Pesaran, M. H., Schuermann, T., & Weiner, S. M. (2004). Modeling regional interdependencies using a global error-correcting macroeconometric model. Journal of Business and Economic Statistics, 22(2), 129-162.	Bernanke, B. S., Boivin, J., & Eliasz, P. S. (2005). Measuring monetary policy. Journal of Political Economy, 113(1), 161-208.
ชื่อเรียกอื่น	Factor models with individual heterogeneity	GVAR, Multi-country VAR	Dynamic factor model with time-varying parameters
ที่เกี่ยวข้อง	3	3	3
สรุป	Interactive Fixed Effects (IFE) extends standard fixed-effects panel models by allowing unit-specific intercepts to vary not just at the individual level but also with unobserved common time-varying factors. Introduced by Bai (2009), it models heterogeneity as the interaction of individual characteristics and common shocks, ideal for studying cross-sectional variation in how units respond to macro conditions. This framework dominates when common factors drive substantial heterogeneity.	Global VAR (GVAR) is a large-scale macroeconomic modeling framework linking multiple countries (or regions) via trade and financial channels, allowing shocks in one country to propagate through the global system. Introduced by Pesaran et al. (2004), it solves the curse of dimensionality in international VAR models by estimating country-specific VARs conditional on foreign variables, then solving a system linking all countries. This approach is invaluable for analyzing global spillovers and international policy coordination.	TVP-FAVAR is a hybrid framework combining factor-augmented VARs with time-varying parameter estimation via Kalman filtering. Introduced by Bernanke et al. (2005) and refined by Primiceri (2005), it extracts latent economic factors (e.g., a 'common monetary policy shock') from high-dimensional data while allowing VAR coefficients to evolve stochastically over time. This framework captures both reduced-dimensionality patterns and structural instability, making it ideal for studying evolving policy regimes and shock dynamics.