방법 비교

	비선형 자기회귀 분산 시차 (NARDL) 모형	조건부 분위수 회귀	평활 전환 자기회귀 (STAR) 모형
분야	계량경제학	계량경제학	계량경제학
계열	Regression model	Regression model	Regression model
기원 연도	2014	1978	1994
창시자	Shin, Yu & Greenwood-Nimmo	Koenker & Bassett	Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002)
유형	Asymmetric cointegration / error-correction model	Conditional quantile regression	Nonlinear time-series regime-switching model
원전	Shin, Y., Yu, B. & Greenwood-Nimmo, M. (2014). Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework. In: Sickles, R. & Horrace, W. (Eds.), Festschrift in Honor of Peter Schmidt. Springer.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Teräsvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89(425), 208–218.
별칭	nonlinear ARDL, asymmetric ARDL, Doğrusal Olmayan ARDL (NARDL)	conditional quantile regression, regression quantiles, Kantil Regresyon	smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR
관련	4	5	4
요약	The NARDL model, introduced by Shin, Yu and Greenwood-Nimmo in 2014, extends the ARDL framework to capture asymmetric long-run and short-run relationships, testing whether positive and negative changes in a regressor affect the dependent variable differently.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.	The Smooth Transition Autoregressive (STAR) model is a nonlinear time-series model, developed in Teräsvirta's 1994 framework, that lets the dynamics move smoothly rather than abruptly between two regimes. The logistic variant (LSTAR) captures asymmetric business cycles and the exponential variant (ESTAR) captures purchasing-power-parity deviations.