পদ্ধতির তুলনা করুন

	PANIC টেস্ট: সাধারণ উপাদান বিয়োজনের সাথে প্যানেল ইউনিট রুট বিশ্লেষণ	CIPS Test	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-section dependence	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)	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
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 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.