Measurement errors in a spatial context

Julie Le Gallo and Bernard Fingleton ()

Regional Science and Urban Economics, 2012, vol. 42, issue 1-2, 114-125

Abstract: Measurement error in an independent variable is one reason why OLS estimates may not be consistent. However, as shown by Dagenais (1994), in some circumstances the OLS bias may be ameliorated somewhat given the presence of serially correlated disturbances, and OLS may prove superior to standard techniques used to correct for serial correlation. This paper considers the case of cross-sectional regression models with measurement errors in the explanatory variables and with spatial dependence. The study focuses on the evidence provided by an empirical illustration and Monte Carlo experiments examining measurement error impact in the presence of autoregressive error processes and autoregressive spatial lags.

Keywords: Measurement error; Spatial autocorrelation; Instrumental variables; GMM; Monte-Carlo simulations (search for similar items in EconPapers)
JEL-codes: C13 C21 R15 (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:regeco:v:42:y:2012:i:1:p:114-125

DOI: 10.1016/j.regsciurbeco.2011.08.004

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