Wald estimator
E766783
The Wald estimator is a statistical method used in econometrics and causal inference to estimate parameters by dividing an estimated effect by its standard error, forming the basis of the Wald test.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Wald estimator canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T8926554 — 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: Wald estimator Context triple: [Abraham Wald, notableConcept, Wald estimator]
-
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.
Spearman–Brown prophecy formula
The Spearman–Brown prophecy formula is a psychometric equation used to predict how changes in test length will affect the reliability of a measurement instrument.
-
C.
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.
-
D.
Taxidea
Taxidea is a genus of mustelid mammals best known for the American badger, a burrowing carnivore native to North America.
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E.
Student’s t-distribution
Student’s t-distribution is a continuous probability distribution used primarily to estimate population means and conduct hypothesis tests when sample sizes are small and population variance is unknown.
- 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: Wald estimator Target entity description: The Wald estimator is a statistical method used in econometrics and causal inference to estimate parameters by dividing an estimated effect by its standard error, forming the basis of the Wald test.
-
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.
Spearman–Brown prophecy formula
The Spearman–Brown prophecy formula is a psychometric equation used to predict how changes in test length will affect the reliability of a measurement instrument.
-
C.
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.
-
D.
Taxidea
Taxidea is a genus of mustelid mammals best known for the American badger, a burrowing carnivore native to North America.
-
E.
Student’s t-distribution
Student’s t-distribution is a continuous probability distribution used primarily to estimate population means and conduct hypothesis tests when sample sizes are small and population variance is unknown.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
econometric method
ⓘ
method of statistical inference ⓘ statistical estimator ⓘ |
| advantage | does not require estimation under the alternative hypothesis for simple tests ⓘ |
| appliesTo |
generalized linear models
ⓘ
linear regression models ⓘ nonlinear regression models ⓘ structural equation models ⓘ |
| assumes |
asymptotic normal distribution of the estimator
ⓘ
large sample size ⓘ regularity conditions for maximum likelihood estimators ⓘ |
| basedOn |
asymptotic normality of estimators
ⓘ
maximum likelihood estimation ⓘ |
| category |
asymptotic test
ⓘ
parametric inference method ⓘ |
| comparedWith |
likelihood ratio estimator
ⓘ
score (Lagrange multiplier) estimator NERFINISHED ⓘ |
| developedIn | 20th century ⓘ |
| hasAlternativeName |
Wald statistic
NERFINISHED
ⓘ
Wald-type estimator NERFINISHED ⓘ |
| hasComponent |
estimated standard error of parameter
ⓘ
point estimate of parameter ⓘ |
| hasForm | parameter estimate divided by its standard error ⓘ |
| introducedBy | Abraham Wald NERFINISHED ⓘ |
| limitation |
can be unreliable when parameter is near the boundary of the parameter space
ⓘ
can perform poorly in small samples ⓘ sensitive to poor standard error estimation ⓘ |
| namedAfter | Abraham Wald NERFINISHED ⓘ |
| relatedTo |
Wald test
NERFINISHED
ⓘ
likelihood ratio test ⓘ score test ⓘ t-statistic ⓘ z-statistic ⓘ |
| usedFor |
construction of confidence intervals
ⓘ
estimating local average treatment effects in simple IV settings ⓘ hypothesis testing ⓘ parameter estimation ⓘ testing joint significance of multiple parameters ⓘ testing linear restrictions on parameters ⓘ testing nonlinear restrictions on parameters ⓘ testing significance of regression coefficients ⓘ |
| usedIn |
causal inference
ⓘ
econometrics ⓘ instrumental variables analysis ⓘ statistics ⓘ |
| yields |
test statistic with asymptotic chi-squared distribution
ⓘ
test statistic with asymptotic standard normal distribution in scalar case ⓘ |
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: Wald estimator Description of subject: The Wald estimator is a statistical method used in econometrics and causal inference to estimate parameters by dividing an estimated effect by its standard error, forming the basis of the Wald test.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.
subject surface form:
Abraham Wald