Сравнение на методи

	PANIC Тест: Анализ на единичен корен в панел с разлагане на общ фактор	Тест CIPS	Динамичен фактор модел
Област	Иконометрия	Иконометрия	Иконометрия
Семейство	Hypothesis test	Hypothesis test	Regression model
Година на възникване	2004	2007	2002
Създател	Jushan Bai & Serena Ng	M. Hashem Pesaran	James Stock & Mark Watson
Тип	Panel unit root test	Panel unit-root test with cross-section dependence	Latent-factor time-series model
Основополагащ източник	Bai, J., & Ng, S. (2004). A PANIC attack on unit roots and cointegration. Econometrica, 72(4), 1127–1177.	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.
Други названия	Panel Analysis of Non-stationarity in Idiosyncratic and Common Components, Bai-Ng PANIC Test, Second-Generation Panel Unit Root Test, Panel Birim Kök Testi (PANIC)	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
Свързани	3	3	2
Резюме	PANIC (Panel Analysis of Non-stationarity in Idiosyncratic and Common Components) is a second-generation panel unit root test introduced by Bai and Ng (2004). It decomposes each panel series into common factors and idiosyncratic components, then tests for unit roots in each part separately, making it robust to cross-sectional dependence — a critical limitation of first-generation tests such as IPS or LLC.	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.