Heckman correction
E428368
The Heckman correction is an econometric technique that adjusts for sample selection bias in regression models by jointly modeling the selection process and the outcome.
All labels observed (3)
| Label | Occurrences |
|---|---|
| Heckman correction canonical | 3 |
| Heckman two-step estimator | 2 |
| Heckman two-step procedure | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4294754 — 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: Heckman correction Context triple: [James Heckman, knownFor, Heckman correction]
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A.
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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B.
LIML
LIML is the ICAO airport code for Milan Linate Airport, a major city airport serving Milan, Italy.
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C.
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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D.
Econometrics
Econometrics is a field of economics that applies statistical and mathematical methods to analyze economic data and test economic theories.
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E.
Lucas critique
The Lucas critique is an influential argument in macroeconomics asserting that policy evaluations based on historical correlations are unreliable because people’s expectations and behavior change systematically when policy rules change.
- 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: Heckman correction Target entity description: The Heckman correction is an econometric technique that adjusts for sample selection bias in regression models by jointly modeling the selection process and the outcome.
-
A.
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.
-
B.
LIML
LIML is the ICAO airport code for Milan Linate Airport, a major city airport serving Milan, Italy.
-
C.
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.
-
D.
Econometrics
Econometrics is a field of economics that applies statistical and mathematical methods to analyze economic data and test economic theories.
-
E.
Lucas critique
The Lucas critique is an influential argument in macroeconomics asserting that policy evaluations based on historical correlations are unreliable because people’s expectations and behavior change systematically when policy rules change.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
econometric method
ⓘ
sample selection correction technique ⓘ |
| addresses |
non-random sample selection
ⓘ
sample selection bias ⓘ |
| alsoKnownAs |
Heckit
ⓘ
Heckman two-step procedure ⓘ
linked to:
Heckman correction
|
| appliesTo |
education attainment models
ⓘ
health economics utilization models ⓘ labor economics wage equations ⓘ regression models with censored samples ⓘ |
| assumes |
correct specification of selection equation
ⓘ
exclusion restriction for identification ⓘ joint normality of error terms ⓘ |
| basedOn | latent variable model ⓘ |
| category |
bias correction method
ⓘ
limited dependent variable model technique ⓘ |
| component |
correlation between selection and outcome errors
ⓘ
outcome equation ⓘ selection equation ⓘ |
| developedBy |
James J. Heckman
ⓘ
linked to:
James Heckman
|
| field |
econometrics
ⓘ
statistics ⓘ |
| goal | obtain unbiased and consistent parameter estimates under sample selection ⓘ |
| hasStep |
compute inverse Mills ratio from selection equation
ⓘ
estimate selection equation by probit ⓘ include inverse Mills ratio in outcome regression ⓘ |
| implementedIn |
Python econometrics libraries
ⓘ
R ⓘ SAS ⓘ Stata ⓘ |
| introducedIn | 1970s ⓘ |
| limitation |
can be unstable with weak exclusion restrictions
ⓘ
sensitive to distributional assumptions ⓘ |
| namedAfter |
James J. Heckman
ⓘ
linked to:
James Heckman
|
| notablePublication | Heckman 1979 sample selection bias paper ⓘ |
| output | selection-corrected parameter estimates ⓘ |
| relatedTo |
Tobit model
ⓘ
endogenous sample selection ⓘ sample selection model ⓘ |
| requires | instrumental variables for robust identification in practice ⓘ |
| usedIn |
microeconometric analysis
ⓘ
policy analysis ⓘ program evaluation ⓘ |
| uses |
inverse Mills ratio
ⓘ
joint modeling of selection and outcome equations ⓘ linear outcome equation ⓘ probit selection equation ⓘ |
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: Heckman correction Description of subject: The Heckman correction is an econometric technique that adjusts for sample selection bias in regression models by jointly modeling the selection process and the outcome.
Referenced by (6)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Heckman correction
linked to: Heckman correction
subject linked to:
“Sample Selection Bias as a Specification Error”
linked to: Heckman correction
subject linked to:
“Sample Selection Bias as a Specification Error”