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Replication Data for: Accounting for Skewed or One-Sided Measurement Error in the Dependent Variable

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: Accounting for Skewed or One-Sided Measurement Error in the Dependent Variable
 
Identifier https://doi.org/10.7910/DVN/IKSE2O
 
Creator Millimet, Daniel
Parmeter, Christopher
 
Publisher Harvard Dataverse
 
Description While classical measurement error in the dependent variable in a linear regression framework results only in a loss of precision, nonclassical measurement error can lead to estimates which are biased and inference which lacks power. Here, we consider a particular type of nonclassical measurement error: skewed errors. Unfortunately, skewed measurement error is likely to be a relatively common feature of many out-
comes of interest in political science research. This study highlights the bias that can result even from relatively "small" amounts of skewed measurement error, particularly if the measurement error is heteroskedastic. We also assess potential solutions to this problem, focusing on the stochastic frontier model and nonlinear least squares. Simulations and three replications highlight the importance of thinking carefully about skewed measurement error, as well as appropriate solutions.
 
Subject Social Sciences
 
Contributor Code Ocean