The Consequences of Non-Classical Measurement Error for Distributional Analysis.
This paper analyzes the consequences of non-classical measurement error for distributional analysis. We show that for a popular set of distributions negative correlation between the measurement error (u) and the true value (y) may reduce the bias in the estimated distribution at every value of y*. For other distributions the impact of non-classical measurement differs throughout results using models of unemployment duration and income.
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