Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Panel Data Coarsened Exact Matching (Panel Data CEM)× | Fiksēto efektu paneļa datu modelis× | |
|---|---|---|
| Nozare≠ | Cēloņsakarību secināšana | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2012 (CEM); 2021 (panel extension) | 2014 |
| Autors≠ | Iacus, King & Porro (CEM, 2012); panel extension via Imai, Kim & Wang (2021) | Hsiao (textbook treatment); within transformation of panel data |
| Tips≠ | Matching / quasi-experimental | Panel data regression |
| Pirmavots≠ | Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. DOI ↗ | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| Citi nosaukumi≠ | Panel CEM, CEM for panel data, coarsened exact matching with panel data | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | Panel Data Coarsened Exact Matching applies the Coarsened Exact Matching (CEM) algorithm to repeated-measures panel data, matching treated and control units within the same coarsened covariate strata across multiple time periods. It balances pre-treatment characteristics before estimating a causal treatment effect, combining the transparency of exact matching with the richer identification available in longitudinal datasets. | The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014). |
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