Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Multilevel Regression and Poststratification× | Dünaamiline paneelmudel× | |
|---|---|---|
| Valdkond≠ | Political Science | Ökonomeetria |
| Perekond | Regression model | Regression model |
| Tekkeaasta≠ | 2004 | 1988–1991 |
| Looja≠ | Gelman and Little (method); Park, Gelman & Bafumi (state-level application) | Arellano & Bond (1991); Holtz-Eakin, Newey & Rosen (1988) |
| Tüüp≠ | Survey small-area estimation model combining multilevel regression with census poststratification | Dynamic regression / GMM estimation |
| Algallikas≠ | 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. DOI ↗ | 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. DOI ↗ |
| Rööpnimetused | 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 |
| Seotud | 5 | 5 |
| Kokkuvõte≠ | 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. |
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