방법 비교

	Panel VARX	글로벌 VAR	시변 모수 요인 증강 VAR (Time-Varying Parameter Factor-Augmented VAR)
분야	계량경제학	계량경제학	계량경제학
계열	Regression model	Regression model	Regression model
기원 연도	2013	2004	2005
창시자	Canova and Ciccarelli	Pesaran, Schuermann, and Weiner	Bernanke, Boivin, and Eliasz
유형	Multi-equation panel model	International system model	Time-varying system
원전	Canova, F., & Ciccarelli, M. (2013). Panel vector autoregressive models: A survey. Advances in Econometrics, 32, 205-246.	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.
별칭	Panel VAR-X	GVAR, Multi-country VAR	Dynamic factor model with time-varying parameters
관련	3	3	3
요약	Panel VARX extends vector autoregression to heterogeneous panels with exogenous variables, enabling simultaneous modeling of multiple endogenous variables alongside observed external factors across many units. Introduced by Holtz-Eakin et al. (1988) and advanced by Canova and Ciccarelli (2013), it captures dynamic relationships within units while allowing parameters to vary across units. This framework is essential for macroeconomic panels and understanding cross-unit heterogeneity in responses to common shocks.	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.