Augmented Mean Group (AMG) Estimator
Also known as: AMG estimator, augmented mean group, Artırılmış Ortalama Grup Tahmincisi (AMG)
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.
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When to use it
Use AMG with macro panels where the time dimension T and the cross-section N are both reasonably large (at least about 50 observations overall, and avoid it when T is below 10), the slope relationship plausibly differs across units, and there is cross-sectional dependence (units share common shocks) — ideally confirmed beforehand with a Pesaran CD test. It is a natural alternative to the CCE Mean Group estimator for this setting.
Strengths & limitations
- Handles cross-sectional dependence by explicitly modelling an unobserved common dynamic process.
- Allows fully heterogeneous slope coefficients across units rather than imposing a single pooled slope.
- Provides a transparent, two-stage alternative to the CCE Mean Group estimator.
- Unreliable when the time dimension is short (T < 10); the unit-by-unit regressions and the common process estimate become noisy.
- Requires reasonably large T and N, so it is unsuited to short or narrow panels.
- Assumes the common factor structure is adequately proxied by the common dynamic process; misspecification of that process biases results.
Frequently asked
How does AMG differ from the CCEMG estimator?
Both target heterogeneous slopes under cross-sectional dependence. CCEMG controls for common factors by adding cross-sectional averages of the variables to each unit's regression, whereas AMG explicitly extracts a common dynamic process from pooled year dummies and augments each unit's regression with it. AMG is offered as a direct alternative to CCEMG.
What is the 'common dynamic process' in AMG?
It is a single time-varying series, recovered from the estimated year dummies of a first-stage pooled regression, that proxies the unobserved common factors shared by all units. Adding it to each unit-level regression is the 'augmentation' that gives the method its name.
Why does the time dimension T matter so much?
AMG fits a separate regression for every unit and estimates a common process over time, so it needs enough time periods to do this reliably. When T is below about 10 the estimator is considered untrustworthy, and reasonably large T and N are required overall.
Do I need to test for cross-sectional dependence first?
Yes. AMG is built for panels where units share common shocks, so you should confirm that cross-sectional dependence is actually present, typically with a Pesaran CD test, before relying on the method.
Sources
- Eberhardt, M. & Teal, F. (2010). Productivity Analysis in Global Manufacturing Production. Economics Series Working Papers, No. 515, University of Oxford. link ↗
- Bond, S. & Eberhardt, M. (2013). Accounting for Unobserved Heterogeneity in Panel Time Series Models. Nuffield College Discussion Paper. link ↗
How to cite this page
ScholarGate. (2026, June 1). Augmented Mean Group (AMG) Estimator. ScholarGate. https://scholargate.app/en/econometrics/amg-estimator
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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