Heckman selection model
E428370
econometric model
limited information maximum likelihood model
sample selection model
two-step estimation procedure
The Heckman selection model is an econometric technique that corrects for sample selection bias in regression analysis by jointly modeling the selection process and the outcome equation.
All labels observed (3)
How this entity was disambiguated
This entity first appeared as the object of triple T4294778 — 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 selection model Context triple: [James Heckman, notableIdea, Heckman selection model]
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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.
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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C.
LIML
LIML is the ICAO airport code for Milan Linate Airport, a major city airport serving Milan, Italy.
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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.
The Theory and Measurement of Demand
The Theory and Measurement of Demand is a foundational economics book by Henry Schultz that rigorously develops statistical and mathematical methods for estimating consumer demand.
- 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 selection model Target entity description: The Heckman selection model is an econometric technique that corrects for sample selection bias in regression analysis by jointly modeling the selection process and the outcome equation.
-
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.
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.
-
C.
LIML
LIML is the ICAO airport code for Milan Linate Airport, a major city airport serving Milan, Italy.
-
D.
Econometrics
Econometrics is a field of economics that applies statistical and mathematical methods to analyze economic data and test economic theories.
-
E.
The Theory and Measurement of Demand
The Theory and Measurement of Demand is a foundational economics book by Henry Schultz that rigorously develops statistical and mathematical methods for estimating consumer demand.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
econometric model
ⓘ
limited information maximum likelihood model ⓘ sample selection model ⓘ two-step estimation procedure ⓘ |
| addresses |
non-random sample selection
ⓘ
sample selection bias ⓘ |
| alternativeName |
Heckit model
ⓘ
linked to:
Heckman
Heckman two-step model ⓘ
linked to:
Heckman selection model
|
| appliesTo |
censored samples
ⓘ
truncated samples ⓘ |
| assumes |
joint normality of error terms
ⓘ
latent variable selection mechanism ⓘ |
| basedOn | probit model for selection equation ⓘ |
| category |
limited dependent variable models
ⓘ
selection bias correction methods ⓘ |
| correctsFor | bias from observing outcomes only for selected samples ⓘ |
| developedBy |
James J. Heckman
ⓘ
linked to:
James Heckman
|
| estimationMethod |
Heckman two-step estimator
ⓘ
linked to:
Heckman correction
full information maximum likelihood ⓘ |
| field |
econometrics
ⓘ
statistics ⓘ |
| hasComponent |
inverse Mills ratio
ⓘ
outcome equation ⓘ selection equation ⓘ |
| hasLimitation |
requires valid exclusion restrictions for robust identification
ⓘ
sensitive to normality assumption ⓘ |
| influenced |
endogenous switching regression models
ⓘ
modern treatment effect models ⓘ |
| introducedIn | 1970s ⓘ |
| mathematicalTool | inverse Mills ratio as selection correction term ⓘ |
| notablePublication | Heckman 1979 sample selection bias paper ⓘ |
| produces | consistent parameter estimates under correct specification ⓘ |
| relatedTo |
Tobit model
ⓘ
endogenous sample selection ⓘ |
| requires |
at least one variable affecting selection but not outcome
ⓘ
exclusion restriction for identification ⓘ |
| typicalStep |
compute inverse Mills ratio from selection equation
ⓘ
estimate probit selection equation ⓘ include inverse Mills ratio in outcome regression ⓘ |
| usedFor |
estimating health care utilization with non-random insurance coverage
ⓘ
estimating returns to education with non-random schooling decisions ⓘ estimating wage equations with labor force participation ⓘ |
| usedIn |
education economics
ⓘ
health economics ⓘ labor economics ⓘ microeconometrics ⓘ program evaluation ⓘ regression analysis ⓘ |
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 selection model Description of subject: The Heckman selection model is an econometric technique that corrects for sample selection bias in regression analysis by jointly modeling the selection process and the outcome equation.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.
James J. Heckman
→
notableWork
→
“The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables”
ⓘ
subject linked to:
Heckman
linked to: Heckman selection model
linked to: Heckman selection model
subject linked to:
“Sample Selection Bias as a Specification Error”