Linganisha mbinu

	Msimamizi wa Wastani Ulioongezwa (Augmented Mean Group - AMG)	Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)	Kielelezo cha Athari Zilizowekwa za Data ya Paneli
Nyanja	Ekonometriki	Ekonometriki	Ekonometriki
Familia	Regression model	Regression model	Regression model
Mwaka wa asili	2010	2019	2014
Mwanzilishi	Eberhardt & Teal; Bond & Eberhardt	Wooldridge (textbook treatment); classical least squares	Hsiao (textbook treatment); within transformation of panel data
Aina	Heterogeneous panel data estimator	Linear regression	Panel data regression
Chanzo asilia	Eberhardt, M. & Teal, F. (2010). Productivity Analysis in Global Manufacturing Production. Economics Series Working Papers, No. 515, University of Oxford.	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.
Majina mbadala	AMG estimator, augmented mean group, Artırılmış Ortalama Grup Tahmincisi (AMG)	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
Zinazohusiana	4	5	5
Muhtasari	The Augmented Mean Group estimator, developed by Eberhardt and Teal (2010), is a panel data method for estimating heterogeneous slope coefficients in the presence of cross-sectional dependence. It approximates the unobserved common dynamic process driving all units and folds it into unit-by-unit regressions, then averages the results.	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).