পদ্ধতির তুলনা করুন

	Multilevel Regression and Poststratification	ডাইনামিক প্যানেল ডেটা মডেল
ক্ষেত্র	Political Science	অর্থমিতি
পরিবার	Regression model	Regression model
উদ্ভবের বছর	2004	1988–1991
প্রবর্তক	Gelman and Little (method); Park, Gelman & Bafumi (state-level application)	Arellano & Bond (1991); Holtz-Eakin, Newey & Rosen (1988)
ধরন	Survey small-area estimation model combining multilevel regression with census poststratification	Dynamic regression / GMM estimation
মৌলিক উৎস	Park, D. K., Gelman, A., & Bafumi, J. (2004). Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls. Political Analysis, 12(4), 375–385.	Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277–297.
অপর নাম	MRP, Mister P, Multilevel regression with poststratification, Small-area opinion estimation	dynamic panel model, panel data model with lagged dependent variable, DPD model, Arellano-Bond model
সম্পর্কিত	5	5
সারসংক্ষেপ	Multilevel regression and poststratification (MRP) estimates opinion or behavior in small subpopulations — states, districts, demographic groups — from a single national survey that is far too small to support direct estimates in each unit. It first fits a multilevel model that predicts the outcome from individual demographic and geographic characteristics, borrowing strength across units through partial pooling, and then poststratifies the predicted values to known population counts of demographic-by-geographic cells. Introduced for state-level opinion by Park, Gelman, and Bafumi (2004) and shown by Lax and Phillips (2009) to outperform disaggregation, MRP has become the standard tool for subnational opinion estimation.	The dynamic panel data model extends standard panel regression by including a lagged value of the outcome variable as a regressor, capturing persistence and adjustment dynamics. Because the lagged dependent variable is correlated with the unit-specific fixed effect, ordinary OLS or within estimators are biased; GMM-based methods using internal instruments are the standard remedy.