방법 비교

	Panel TGARCH (Threshold GARCH for Panel Data)	패널 EGARCH	패널 데이터 고정 효과 모형
분야	계량경제학	계량경제학	계량경제학
계열	Regression model	Regression model	Regression model
기원 연도	1993–1994 (panel extension: 2000s onward)	1991 (EGARCH); panel extensions widely used from 2000s	2014
창시자	Glosten, Jagannathan & Runkle (1993); Zakoian (1994); extended to panel settings by subsequent applied finance literature	Daniel B. Nelson (EGARCH); panel extension by applied econometrics literature	Hsiao (textbook treatment); within transformation of panel data
유형	Asymmetric conditional volatility model	Volatility model	Panel data regression
원전	Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779–1801.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
별칭	Panel GJR-GARCH, Panel Asymmetric GARCH, Panel Threshold GARCH, TGARCH panel model	Panel EGARCH model, panel exponential GARCH, EGARCH for panel data, cross-sectional EGARCH	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
관련	4	4	5
요약	Panel TGARCH extends the Threshold GARCH (GJR-GARCH) model to panel data, allowing each cross-sectional unit to exhibit asymmetric volatility responses — where negative shocks generate larger variance increases than positive shocks of the same magnitude — while exploiting the cross-sectional dimension to obtain more efficient parameter estimates.	Panel EGARCH extends Nelson's (1991) Exponential GARCH model to a panel setting, allowing conditional variance to evolve asymmetrically over time for each cross-sectional unit. The log specification ensures non-negative variance without parameter constraints, and the leverage term distinguishes whether negative shocks amplify volatility more than positive ones of equal magnitude.	The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).