পদ্ধতির তুলনা করুন

	PANIC টেস্ট: সাধারণ উপাদান বিয়োজনের সাথে প্যানেল ইউনিট রুট বিশ্লেষণ	ক্রস-সেকশনালি অগমেন্টেড ডিকি-ফুলার (CADF) পরীক্ষা	ডাইনামিক ফ্যাক্টর মডেল
ক্ষেত্র	অর্থমিতি	অর্থমিতি	অর্থমিতি
পরিবার	Hypothesis test	Hypothesis test	Regression model
উদ্ভবের বছর	2004	2007	2002
প্রবর্তক	Jushan Bai & Serena Ng	M. Hashem Pesaran	James Stock & Mark Watson
ধরন	Panel unit root test	Panel unit-root test with cross-sectional augmentation	Latent-factor time-series model
মৌলিক উৎস	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.
অপর নাম	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)	Cross-Sectionally Augmented ADF, Panel CADF Test, Pesaran Panel Unit Root Test, CADF Birim Kök Testi	Diffusion Index Model, Large-Scale Factor Model, Approximate Factor Model, Dinamik Faktör Modeli
সম্পর্কিত	3	3	2
সারসংক্ষেপ	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 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.	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.