Covariate Measurement Error: Bias Reduction under Response-based Sampling

Esmeralda Ramalho ()

CEFAGE-UE Working Papers from University of Evora, CEFAGE-UE (Portugal)

Abstract: In this paper we propose a general framework to deal with the presence of covariate measurement error (CME) in response-based (RB) samples. Using ChesherÂ’s (1991) methodology, we obtain a small error variance approximation for the contaminated sampling distributions that characterise RB samples with CME. Then, following Chesher (2000), we develop generalised method of moments (GMM) estimators that reduce the bias of the most well known likelihood-based estimators for RB samples which ignore the existence of CME and derive a score test to detect the presence of this type of measurement error. Our approach only requires the specification of the conditional distribution of the response variable given the latent covariates and the classical additive measurement error model assumption, the availability of information on both the marginal probability of the strata in the population and the variance of the measurement error not being essential. Monte Carlo evidence is presented which suggests that, in RB samples of moderate sizes, the bias-reduced GMM estimators perform well.

Keywords: Response-based samples; Covariate measurement error; Generalized method of moments estimation; Score tests (search for similar items in EconPapers)
JEL-codes: C51 C52 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2009
New Economics Papers: this item is included in nep-ecm
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Related works:
Journal Article: Covariate Measurement Error: Bias Reduction under Response-Based Sampling (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:cfe:wpcefa:2009_15

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