विधियों की तुलना करें

	Multilevel Regression and Poststratification	गत्यात्मक पैनल डेटा मॉडल
क्षेत्र	Political Science	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	2004	1988–1991
प्रवर्तक	Gelman and Little (method); Park, Gelman & Bafumi (state-level application)	Arellano & Bond (1991); Holtz-Eakin, Newey & Rosen (1988)
प्रकार	Survey small-area estimation model combining multilevel regression with census poststratification	Dynamic regression / GMM estimation
मौलिक स्रोत	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.
उपनाम	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
संबंधित	5	5
सारांश	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.