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

Daniel Millimet and Christopher Parmeter

No 12576, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: While classical measurement error in the dependent variable in a linear regression framework results only in a loss of precision, non-classical measurement error can lead to estimates which are biased and inference which lacks power. Here, we consider a particular type of non-classical 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: stochastic frontier; nonlinear least squares; measurement error (search for similar items in EconPapers)
JEL-codes: C18 C51 (search for similar items in EconPapers)
Pages: 58 pages
Date: 2019-08
New Economics Papers: this item is included in nep-ecm, nep-eff and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Published - published in: Political Analysis, 2022, 30, 66-88

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Journal Article: Accounting for Skewed or One-Sided Measurement Error in the Dependent Variable (2022)
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