방법 비교

	교차-단면 증강 디키-풀러 (CADF) 검정	CIPS 검정	동적 요인 모형
분야	계량경제학	계량경제학	계량경제학
계열	Hypothesis test	Hypothesis test	Regression model
기원 연도	2007	2007	2002
창시자	M. Hashem Pesaran	M. Hashem Pesaran	James Stock & Mark Watson
유형	Panel unit-root test with cross-sectional augmentation	Panel unit-root test with cross-section dependence	Latent-factor time-series model
원전	Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265–312.	Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265–312.	Stock, J. H., & Watson, M. W. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics, 20(2), 147–162.
별칭	Cross-Sectionally Augmented ADF, Panel CADF Test, Pesaran Panel Unit Root Test, CADF Birim Kök Testi	Pesaran CIPS Test, Cross-Sectionally Augmented IPS, Second-Generation Panel Unit-Root Test, CIPS Birim Kök Testi	Diffusion Index Model, Large-Scale Factor Model, Approximate Factor Model, Dinamik Faktör Modeli
관련	3	3	2
요약	The Cross-sectionally Augmented Dickey-Fuller (CADF) test, introduced by Pesaran (2007), is a second-generation panel unit-root test designed to handle cross-sectional dependence among panel units. Unlike first-generation panel unit-root tests that assume cross-sectional independence, the CADF test augments individual ADF regressions with cross-sectional averages of lagged levels and first differences, making it suitable for macro-panels and cross-country studies where common factors drive co-movement.	The CIPS test, introduced by Pesaran (2007), is a second-generation panel unit-root test designed for panels in which the cross-sectional units share unobserved common factors that induce cross-section dependence. By augmenting each individual ADF regression with cross-sectional averages and their lags, the CIPS test accounts for this dependence and produces reliable inference where first-generation tests such as the original IPS test break down. It is widely applied in macroeconomic and finance panels where shocks propagate across countries or regions.	A Dynamic Factor Model (DFM) extracts a small number of latent common factors from a large panel of economic time series and uses those factors to forecast or nowcast a target variable. Formalized for macroeconomic forecasting by James Stock and Mark Watson in their 2002 Journal of Business & Economic Statistics paper, DFMs handle hundreds of indicators simultaneously while avoiding the curse of dimensionality that plagues traditional multivariate models.