Generalized method of moments
E1046128
The generalized method of moments is an econometric estimation technique that uses sample moments to infer model parameters without requiring full specification of the underlying probability distribution.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Generalized method of moments canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T13547076 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Generalized method of moments Context triple: [Lars Peter Hansen, notableWork, Generalized method of moments]
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A.
The Probability Approach in Econometrics
The Probability Approach in Econometrics is Trygve Haavelmo’s landmark work that founded modern econometrics by rigorously formulating economic relationships within a probabilistic, statistical framework.
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B.
Econometric Theory
Econometric Theory is a scholarly journal that publishes research on the theoretical foundations and methodological developments of econometrics.
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C.
Econometric Model of the United States
Econometric Model of the United States is a large-scale macroeconometric model developed to analyze and forecast the U.S. economy, particularly associated with the pioneering work of economist Lawrence Klein.
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D.
Frisch–Waugh–Lovell theorem
The Frisch–Waugh–Lovell theorem is a fundamental result in econometrics that shows how the coefficients of a multiple linear regression can be obtained by first partialling out (regressing out) other explanatory variables.
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E.
Rational Expectations and Econometric Practice
Rational Expectations and Econometric Practice is an influential collection of essays that helped formalize and popularize the rational expectations approach in macroeconomics and econometrics.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Generalized method of moments Target entity description: The generalized method of moments is an econometric estimation technique that uses sample moments to infer model parameters without requiring full specification of the underlying probability distribution.
-
A.
The Probability Approach in Econometrics
The Probability Approach in Econometrics is Trygve Haavelmo’s landmark work that founded modern econometrics by rigorously formulating economic relationships within a probabilistic, statistical framework.
-
B.
Econometric Theory
Econometric Theory is a scholarly journal that publishes research on the theoretical foundations and methodological developments of econometrics.
-
C.
Econometric Model of the United States
Econometric Model of the United States is a large-scale macroeconometric model developed to analyze and forecast the U.S. economy, particularly associated with the pioneering work of economist Lawrence Klein.
-
D.
Frisch–Waugh–Lovell theorem
The Frisch–Waugh–Lovell theorem is a fundamental result in econometrics that shows how the coefficients of a multiple linear regression can be obtained by first partialling out (regressing out) other explanatory variables.
-
E.
Rational Expectations and Econometric Practice
Rational Expectations and Econometric Practice is an influential collection of essays that helped formalize and popularize the rational expectations approach in macroeconomics and econometrics.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
econometric estimation method
ⓘ
method of moments estimator ⓘ statistical estimation method ⓘ |
| abbreviation | GMM ⓘ |
| allows |
more moment conditions than parameters
ⓘ
overidentified models ⓘ |
| appliedIn |
finance
ⓘ
industrial organization ⓘ labor economics ⓘ macroeconomics ⓘ |
| associatedTest |
Hansen J test
ⓘ
linked to:
Sargan test
overidentifying restrictions test ⓘ |
| assumes |
central limit theorem for sample moments
ⓘ
law of large numbers ⓘ |
| basedOn |
moment conditions
ⓘ
sample moments ⓘ |
| canHandle |
autocorrelation
ⓘ
heteroskedasticity ⓘ |
| coreIdea | choose parameters so that sample moments match theoretical moments implied by the model ⓘ |
| doesNotRequire | full specification of the underlying probability distribution ⓘ |
| field |
econometrics
ⓘ
statistics ⓘ |
| generalizes | classical method of moments ⓘ |
| hasLimitation |
finite-sample bias
ⓘ
sensitivity to choice of instruments ⓘ sensitivity to choice of weighting matrix ⓘ |
| hasProperty |
asymptotic normality of estimators
ⓘ
consistency under correct specification of moment conditions ⓘ efficiency when optimal weighting matrix is used ⓘ |
| input |
sample data
ⓘ
theoretical moment conditions ⓘ |
| introducedBy | Lars Peter Hansen ⓘ |
| introducedInYear | 1982 ⓘ |
| optimizationCriterion | quadratic form in sample moment conditions ⓘ |
| output |
estimated covariance matrix of parameters
ⓘ
parameter estimates ⓘ |
| publishedIn | Econometrica ⓘ |
| relatedTo |
generalized empirical likelihood
ⓘ
instrumental variables estimation ⓘ |
| requires | valid instruments when regressors are endogenous ⓘ |
| specialCase | two-stage least squares ⓘ |
| usedFor |
estimation of Euler equations
ⓘ
estimation of asset pricing models ⓘ estimation of dynamic panel data models ⓘ |
| uses |
estimating equations
ⓘ
weighting matrix ⓘ |
| variant |
dynamic panel GMM
ⓘ
linear GMM ⓘ nonlinear GMM ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Generalized method of moments Description of subject: The generalized method of moments is an econometric estimation technique that uses sample moments to infer model parameters without requiring full specification of the underlying probability distribution.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.