Bias From Classical and Other Forms of Measurement Error

By D. Hyslop, Guido W. Imbens. 2001

240 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/af35c00363c9b0ac1ee478a2f8ea93276275d7f0

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Citations in the corpus

The Elephant in the Corner: A Cautionary Tale About Measurement Error in Treatment Effects Models (2010, unknown): “[34] Hyslop, R.”
Measurement Error Mechanisms Matter: Agricultural Intensification with Farmer Misperceptions and Misreporting (2019, unknown): “In Appendix A, we outline a second such process, in which survey respondents report their best estimate of a key explanatory variable (e.g., plot size) based in part on information regarding a proxy closely correlated with the dependent variable (Hyslop and Imbens, 2001).”
Identification and Estimation of Regression Models with Misclassification (2006, unknown): “Imbens and Hyslop (2000) interpret the problem within a prediction model in which the errors are correlated with the true values and discuss the bias introduced in estimation procedures if measurement errors followed their model.”
Estimating Network Economies in Retail Chains: A Revealed Preference Approach (2010, background): “The median population (26,450) and interquartile range (18,420; 38,240) for markets that contain just one store suggest that it takes about 25,000 people to induce the initial entry.”
Do Labor Statistics Depend on How and to Whom the Questions are Asked? Results from a Survey Experiment in Tanzania (2011, unknown): “…they do not have exact information (this may be the case of a proxy respondent) it is possible that, in the linear regression framework, biases in the coefficients of interest are introduced – especially if both regressor and outcome variables are measured with error (Hyslop and Imbens, 2001).”
Assessing the credibility of instrumental variables inference with imperfect instruments via sensitivity analysis (2009, background): “Hausman and W atson (1985) and Hyslop and Imbens (2001) consider more sophisticated specifications of the measurement error process.”
The Costs of Hiring Skilled Workers (2012, background): “In this case, the measurement error is not predictable by the reported value, which is referred to as ”optimal prediction error” in the literature (Hyslop and Imbens, 2001).”
Parks versus PES: Evaluating direct and incentive-based land conservation in Mexico (2017, unknown): “In a locality fixed effects regression, these differences may create measurement error that can both introduce bias and affect precision (Hyslop and Imbens 2001).”
Correlated non-classical measurement errors, ‘Second best’ policy inference, and the inverse size-productivity relationship in agriculture (2019, methodology): “Or one mismeasured variable might be used by a respondent to generate an optimal prediction of another variable (Hyslop and Imbens (2001)), resulting in correlated”
Preferences and Heterogeneous Treatment Effects in a Public School Choice Lottery (2006, background): “Estimating the Impact of Attending a 1 st Choice School on Academic”
Measurement of Higher Education in the Census and Current Population Survey (2003, methodology): “…that the measurement error is uncorrelated with the regression residuals is used in establishing the familiar attenuation bias result and is necessary for the applicationof models of nonclassical measurement error such as Kane et al. (1999), Black et al. (2000), or Hyslop and Imbens (2001).”
Stock Market Wealth and the Real Economy: A Local Labor Market Approach (2019, background): “Hyslop and Imbens (2001) provide a more general discussion of measurement error that does not lead to biased estimation.”