השוואת שיטות

	מבחן ה-Cross-sectionally Augmented Dickey-Fuller (CADF)	מבחן CIPS	מודל גורמים דינמי
תחום	אקונומטריקה	אקונומטריקה	אקונומטריקה
משפחה	Hypothesis test	Hypothesis test	Regression model
שנת המקור	2007	2007	2002
הוגה השיטה	M. Hashem Pesaran	M. Hashem Pesaran	James Stock & Mark Watson
סוג	Panel unit-root test with cross-sectional augmentation	Panel unit-root test with cross-section dependence	Latent-factor time-series model
מקור מכונן	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.
כינויים	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
קשורות	3	3	2
תקציר	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.