Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Standartkļūdas, kas ir izturīgas pret klasteru ietekmi× | Parastā mazāko kvadrātu (OLS) regresija× | |
|---|---|---|
| Nozare≠ | Statistika | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1986 | 2019 |
| Autors≠ | Liang & Zeger (GEE sandwich); Cameron & Miller (practitioner synthesis) | Wooldridge (textbook treatment); classical least squares |
| Tips≠ | Robust variance estimation for regression | Linear regression |
| Pirmavots≠ | Liang, K. Y. & Zeger, S. L. (1986). Longitudinal Data Analysis Using Generalized Linear Models. Biometrika, 73(1), 13-22. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Citi nosaukumi | clustered standard errors, cluster-robust inference, clustered variance estimator, Küme Robust Standart Hatalar | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | Cluster-robust standard errors correct the variance of regression coefficients when observations are correlated within clusters such as schools, hospitals, or regions. The clustered sandwich estimator grew out of Liang & Zeger's (1986) generalized estimating equations and was synthesized for applied work by Cameron & Miller (2015), delivering valid inference when ordinary standard errors would be too small. | 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). |
| ScholarGateDatu kopa ↗ |
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