Durbin–Wu–Hausman test¶
The Durbin–Wu–Hausman test (also called Hausman specification test) is a statistical hypothesis test in econometrics named after James Durbin, De-Min Wu, and Jerry A.
Core Idea¶
Durbin–Wu–Hausman test is treated here as the recurring mathematics, logic, and statistics identity summarized by this source-grounded definition: The Durbin–Wu–Hausman test (also called Hausman specification test) is a statistical hypothesis test in econometrics named after James Durbin, De-Min Wu, and Jerry A. The Durbin–Wu–Hausman test (also called Hausman specification test) is a statistical hypothesis test in econometrics named after James Durbin, De-Min Wu, and Jerry A. The test evaluates the consistency of an estimator when compared to an alternative, less efficient estimator which is already known to be consistent.
Scope of Application¶
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Details. This test can be used to check for the endogeneity of a variable (by comparing instrumental variable (IV) estimates to ordinary least squares (OLS) estimates).
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Details. It can also be used to check the validity of extra instruments by comparing IV estimates using a full set of instruments Z to IV estimates that use a proper subset.
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Panel data. The Hausman test can be used to differentiate between fixed effects model and random effects model in panel analysis.
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Derivation. Consider the function : q=b0-b1\Rightarrow \operatorname{plim}q=0.
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Details. Consider the linear model y = Xb + e, where y is the dependent variable and X is vector of regressors, b is a vector of coefficients and e is the error term.
Clarity¶
A clear use of Durbin–Wu–Hausman test names the carrier, the operative relation, and the conditions under which the source treats the identity as present. The minimal definition is The Durbin–Wu–Hausman test (also called Hausman specification test) is a statistical hypothesis test in econometrics named after James Durbin, De-Min Wu, and Jerry A.
Manages Complexity¶
Durbin–Wu–Hausman test compresses multiple mathematics, logic, and statistics details into a stable diagnostic relation. The source shows both the central mechanism—consider the linear model y = Xb + e, where y is the dependent variable and X is vector of regressors, b is a vector of coefficients and e is the error term.—and the practical consequence—under the null hypothesis, this statistic has asymptotically the chi-squared distribution with.
Abstract Reasoning¶
- Type the carrier. Identify the mathematics, logic, and statistics entities to which the claim applies.
- State the relation. Use the source-grounded identity: The Durbin–Wu–Hausman test (also called Hausman specification test) is a statistical hypothesis test in econometrics named after James Durbin, De-Min Wu, and Jerry A.
- Check operation and conditions. Under the null hypothesis, both of these estimators are consistent, but b 1 is efficient (has the smallest asymptotic variance), at least in the class of estimators containing b 0 . 4.
Knowledge Transfer¶
Within the home domain. Knowledge about Durbin–Wu–Hausman test transfers literally when a new case preserves the same carrier type, relation, and recognition test. This test can be used to check for the endogeneity of a variable (by comparing instrumental variable (IV) estimates to ordinary least squares (OLS) estimates). It can also be used to check the validity of extra instruments by comparing IV estimates using a full set of instruments Z to IV estimates that use a proper subset of Z. Beyond the home domain. No canonical parent is asserted for Durbin–Wu–Hausman test.
Neighborhood in Abstraction Space¶
Durbin–Wu–Hausman test sits in a crowded region of the domain-specific corpus (30th percentile for distinctiveness): several abstractions share nearly its structure, so a description that fits it tends to fit its neighbors too.
Family — Unclustered & Miscellaneous (2551 abstractions)
Nearest neighbors
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- S-procedure — 0.89
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Computed from structural-signature embeddings · 2026-10-08