Stein-rule least squares estimation: A heuristic for fallible data

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Abstract

For fallible data, ‘errors-in-variables’, a Steinian estimation of the independent variable yields a consistent and potentially unbiased estimator of the regression coefficient. This estimator suggests a heuristic for economic research and is illustrated by the permanent income hypothesis.

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Cited by (9)

  • Improved estimation in multiple linear regression models with measurement error and general constraint

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    Citation Excerpt :

    The aforementioned estimation techniques have received much attention recently in linear regression model when the covariates are measured with errors. Stanley [9,10] revealed that JSTE can eliminate inconsistency of the classical least squares estimators. Shalabh [11] studied properties of JSTE when the covariance matrix of the measurement errors is known.

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