Econometric Analysis of Cross Section and Panel Data¶
Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data. MIT Press.
Cited by¶
3 citations across 3 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Frictionless Benchmark Reasoning
- Statistics: ordinary least squares under its ideal assumptions is BLUE; applied econometrics is the catalog of named violations — heteroscedasticity, autocorrelation, endogeneity, mis-specification, measurement error — each with its own diagnostic and remedy.
This sourceStandard reference establishing OLS as BLUE under the Gauss–Markov assumptions and cataloguing named violations — heteroscedasticity, autocorrelation, endogeneity, measurement error — with diagnostics and remedies.
- Statistics: ordinary least squares under its ideal assumptions is BLUE; applied econometrics is the catalog of named violations — heteroscedasticity, autocorrelation, endogeneity, mis-specification, measurement error — each with its own diagnostic and remedy.
Domain-specific¶
- Attenuation Bias
- When the error is nonclassical — correlated with the true value or with y — the bias can point in either direction or even inflate the coefficient, so "noise makes my estimate conservative" is a claim about the error structure, not an unconditional fact. Not a bias from error in the outcome variable. Classical random noise on the dependent variable inflates standard errors but leaves the coefficient unbiased
This sourceThe econometrics text whose measurement-error chapter separates error in the dependent variable from the classical error-in-the-regressor case.
Supported in partVerified against the work's full text
“error in the [dependent] variable we begin with the case where the [dependent] variable is the”
- When the error is nonclassical — correlated with the true value or with y — the bias can point in either direction or even inflate the coefficient, so "noise makes my estimate conservative" is a claim about the error structure, not an unconditional fact. Not a bias from error in the outcome variable. Classical random noise on the dependent variable inflates standard errors but leaves the coefficient unbiased
- Endogeneity
- Endogeneity is the condition in which a regressor — an explanatory variable on the right-hand side of a statistical model — is correlated with the model's error term, so that the ordinary least squares estimator (and most regression-style estimators) produce coefficient estimates that are biased and inconsistent for the causal effect of that variable on the outcome
This sourceWooldridge's graduate text on cross-section and panel data, whose chapters on endogenous explanatory variables and instrumental variables treat the violation of a regressor's exogeneity and the IV remedy.
Supported in partVerified against the work's full text
“each [explanatory] variable is necessarily exogenous. In applied econometrics, [endogeneity] usually”
- Endogeneity is the condition in which a regressor — an explanatory variable on the right-hand side of a statistical model — is correlated with the model's error term, so that the ordinary least squares estimator (and most regression-style estimators) produce coefficient estimates that are biased and inconsistent for the causal effect of that variable on the outcome
Verification¶
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