“Sample Selection Bias as a Specification Error”
E428372
“Sample Selection Bias as a Specification Error” is a landmark econometrics paper by James Heckman that introduced the Heckman correction for dealing with non-randomly selected samples in statistical analysis.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Heckman 1979 sample selection bias paper | 2 |
| “Sample Selection Bias as a Specification Error” canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T4294783 — 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: “Sample Selection Bias as a Specification Error” Context triple: [James Heckman, publication, “Sample Selection Bias as a Specification Error”]
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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.
A Solution to the Ecological Inference Problem
A Solution to the Ecological Inference Problem is a influential methodological book by political scientist Gary King that introduces statistical techniques for inferring individual-level behavior from aggregate data.
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C.
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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D.
Neyman–Pearson theory of hypothesis testing
The Neyman–Pearson theory of hypothesis testing is a foundational statistical framework that formalizes how to construct and evaluate tests for competing hypotheses using concepts like Type I and Type II errors and power.
-
E.
“Statistical Confluence Analysis by Means of Complete Regression Systems”
“Statistical Confluence Analysis by Means of Complete Regression Systems” is a foundational econometric work by Ragnar Frisch that develops a systematic regression-based framework for analyzing interdependent economic relationships.
- 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: “Sample Selection Bias as a Specification Error” Target entity description: “Sample Selection Bias as a Specification Error” is a landmark econometrics paper by James Heckman that introduced the Heckman correction for dealing with non-randomly selected samples in statistical analysis.
-
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.
A Solution to the Ecological Inference Problem
A Solution to the Ecological Inference Problem is a influential methodological book by political scientist Gary King that introduces statistical techniques for inferring individual-level behavior from aggregate data.
-
C.
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.
-
D.
Neyman–Pearson theory of hypothesis testing
The Neyman–Pearson theory of hypothesis testing is a foundational statistical framework that formalizes how to construct and evaluate tests for competing hypotheses using concepts like Type I and Type II errors and power.
-
E.
“Statistical Confluence Analysis by Means of Complete Regression Systems”
“Statistical Confluence Analysis by Means of Complete Regression Systems” is a foundational econometric work by Ragnar Frisch that develops a systematic regression-based framework for analyzing interdependent economic relationships.
- F. None of above. chosen
Statements (45)
| Predicate | Object |
|---|---|
| instanceOf |
academic paper
ⓘ
econometrics paper ⓘ |
| addressesProblem |
bias in parameter estimates due to non-random sample selection
ⓘ
inconsistency of ordinary least squares under sample selection ⓘ omitted variable bias arising from unobserved selection mechanisms ⓘ |
| assumption |
existence of at least one variable that affects selection but not the outcome (exclusion restriction)
ⓘ
joint normality of error terms in selection and outcome equations ⓘ |
| author |
James Heckman
ⓘ
James J. Heckman ⓘ
linked to:
James Heckman
|
| citedFor |
Heckman correction
ⓘ
sample selection model ⓘ treatment of non-random sample selection in regression analysis ⓘ |
| estimationTechnique |
full information maximum likelihood
ⓘ
two-step least squares-type procedure ⓘ |
| field |
econometrics
ⓘ
statistics ⓘ |
| impact |
became a standard reference for dealing with selection bias in empirical work
ⓘ
contributed to James Heckman receiving the Nobel Prize in Economic Sciences ⓘ |
| influenced |
applied health economics
ⓘ
labor economics ⓘ microeconometrics ⓘ policy evaluation methods ⓘ program evaluation ⓘ |
| keyIdea |
interpreting selection bias as a specification error in regression models
ⓘ
modeling the selection process jointly with the outcome equation ⓘ using an explicit selection equation to correct for non-random sampling ⓘ |
| language | English ⓘ |
| mainConcept |
Heckman selection model
ⓘ
endogenous sample selection ⓘ non-randomly selected samples ⓘ sample selection bias ⓘ selection models ⓘ |
| mainContribution |
development of a two-step estimation procedure for sample selection models
ⓘ
formal treatment of sample selection bias as a specification error ⓘ introduction of the Heckman correction ⓘ |
| methodologicalApproach |
structural modeling of selection and outcome
ⓘ
two-equation system estimation ⓘ |
| proposedMethod |
Heckman two-step estimator
ⓘ
linked to:
Heckman correction
maximum likelihood estimation of selection models ⓘ |
| publishedIn | Econometrica ⓘ |
| relatedConcept |
censored regression models
ⓘ
endogeneity ⓘ omitted variable bias ⓘ truncated samples ⓘ |
| yearPublished | 1979 ⓘ |
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: “Sample Selection Bias as a Specification Error” Description of subject: “Sample Selection Bias as a Specification Error” is a landmark econometrics paper by James Heckman that introduced the Heckman correction for dealing with non-randomly selected samples in statistical analysis.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.
subject linked to:
Heckman
linked to: “Sample Selection Bias as a Specification Error”
linked to: “Sample Selection Bias as a Specification Error”