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| Model Kesilahan Rawak (Random Effects Panel Model)× | Regresi Kuasa Dua Terkecil Biasa (OLS)× | Model Kesan Tetap Data Panel× | |
|---|---|---|---|
| Bidang | Ekonometrik | Ekonometrik | Ekonometrik |
| Keluarga | Regression model | Regression model | Regression model |
| Tahun asal≠ | 1978 | 2019 | 2014 |
| Pengasas≠ | Baltagi (textbook treatment); Hausman specification test | Wooldridge (textbook treatment); classical least squares | Hsiao (textbook treatment); within transformation of panel data |
| Jenis≠ | Panel data regression | Linear regression | Panel data regression |
| Sumber perintis≠ | Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46(6), 1251-1271. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| Alias | random effects panel regression, RE estimator, GLS panel estimator, Panel Rassal Etkiler Modeli | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli |
| Berkaitan | 5 | 5 | 5 |
| Ringkasan≠ | The random effects model is a panel data estimator that explains an outcome using both within-unit and between-unit variation, treating the unobserved unit-specific heterogeneity as a random, normally distributed term rather than a fixed parameter. Its validity is judged with the Hausman (1978) specification test, and it is developed in standard treatments such as Baltagi's Econometric Analysis of Panel Data. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). | 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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