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Measurement error is a pervasive problem in economics and other social and behavioral sciences. Estimators that ignore the issue are generally inconsistent, and more information or stronger assumptions are necessary to correct for measurement error. Panel data are a source of more information, but consistent estimators still require some additional information or assumptions. This chapter gives an overview of the measurement error problem, with an emphasis on the linear regression model for panel data, and shows various ways in which panel data can be helpful to obtain consistent estimators. More advanced topics, such as non linear models and nonclassical measurement error are also discussed.
Identification, Measurement error, Structural equation modeling (SEM), Validation studies, Berkson model, Disclosure avoidance, Anderson-Hsiao estimator, Reliability, Euler equations, Panel data
Identification, Measurement error, Structural equation modeling (SEM), Validation studies, Berkson model, Disclosure avoidance, Anderson-Hsiao estimator, Reliability, Euler equations, Panel data
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 7 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |