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| Model Seleksi Sampel Heckman (Heckit / Tobit Tipe II)× | Regresi Logistik× | Model Efek Tetap Data Panel× | |
|---|---|---|---|
| Bidang≠ | Ekonometrika | Statistika Penelitian | Ekonometrika |
| Keluarga≠ | Regression model | Process / pipeline | Regression model |
| Tahun asal≠ | 1979 | 1958 | 2014 |
| Pencetus≠ | James J. Heckman | David Roxbee Cox | Hsiao (textbook treatment); within transformation of panel data |
| Tipe≠ | Two-step sample selection model | Method | Panel data regression |
| Sumber perintis≠ | Heckman, J. J. (1979). Sample Selection Bias as a Specification Error. Econometrica, 47(1), 153–161. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| Alias≠ | heckit, tobit type II, sample selection model, Heckman Seçim Modeli (Heckit / Tobit II) | logit model, binomial logistic regression, LR | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli |
| Terkait≠ | 4 | 3 | 5 |
| Ringkasan≠ | The Heckman selection model, introduced by James J. Heckman in 1979, is a two-step model that corrects sample selection bias when the outcome is only observed for a non-random subset of cases. A probit selection equation models who is observed, and the outcome equation then corrects for the resulting bias using the inverse Mills ratio. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. | 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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