手法を比較

	Augmented Mean Group (AMG) 推定量の概要	パネル共和分検定（ペドロニ、カオ、ウェスターランド）	パネルデータ固定効果モデル
分野	計量経済学	計量経済学	計量経済学
系統	Regression model	Regression model	Regression model
提唱年	2010	2004	2014
提唱者	Eberhardt & Teal; Bond & Eberhardt	Pedroni; Kao; Westerlund	Hsiao (textbook treatment); within transformation of panel data
種類	Heterogeneous panel data estimator	Panel cointegration test	Panel data regression
原典	Eberhardt, M. & Teal, F. (2010). Productivity Analysis in Global Manufacturing Production. Economics Series Working Papers, No. 515, University of Oxford.	Pedroni, P. (2004). Panel Cointegration: Asymptotic and Finite Sample Properties of Pooled Time Series Tests with an Application to the PPP Hypothesis. Econometric Theory, 20(3), 597–625.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
別名	AMG estimator, augmented mean group, Artırılmış Ortalama Grup Tahmincisi (AMG)	Pedroni cointegration test, Kao cointegration test, Westerlund cointegration test, panel long-run equilibrium tests	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
関連	4	3	5
概要	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.	Panel cointegration tests check whether a set of integrated variables share a stable long-run equilibrium relationship across a panel of cross-sectional units. Pedroni (1999, 2004) provides heterogeneous-panel tests with seven statistics, Kao (1999) gives an ADF-based homogeneous-panel test, and Westerlund (2007) adds error-correction-based tests robust to structural breaks and cross-sectional dependence.	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).