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

Published online by Cambridge University Press:  14 January 2021

Daniel L. Millimet  and
Christopher F. Parmeter Open the ORCID record for Christopher F. Parmeter [Opens in a new window]
Daniel L. Millimet
Affiliation:
Department of Economics, Southern Methodist University, Dallas, TX75275-0496, USA Institute of Labor Economics, 53113Bonn, Germany
Christopher F. Parmeter*
Affiliation:
Miami Herbert Business School, University of Miami, Coral Gables, FL33146, USA. Email: [email protected]
*
Corresponding author Christopher F. Parmeter
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Abstract

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 outcomes 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.

Keywords

measurement errorstochastic frontierheteroskedasticity
Type
Article
Information
Political Analysis , Volume 30 , Issue 1 , January 2022 , pp. 66 - 88
Copyright
© The Author(s) 2021. Published by Cambridge University Press on behalf of the Society for Political Methodology

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Footnotes

Edited by Jeff Gill

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Millimet and Parmeter Dataset

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https://doi.org/10.7910/DVN/IKSE2O
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Millimet and Parmeter supplementary material

Appendix 1

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Supplementary material: PDF

Millimet and Parmeter supplementary material

Appendix 2

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