Le Gallo, J. and Fingleton, B. (2012) Measurement errors in a spatial context. Regional Science and Urban Economics, 42 (1-2). pp. 114-125. ISSN 0166-0462Full text not available in this repository. (Request a copy from the Strathclyde author)
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:||autoregressive model, Monte-Carlo simulations, measurement error, spatial autocorrelation, matrices, instrumental variables, Economic Theory, Economics and Econometrics, Urban Studies|
|Subjects:||Social Sciences > Economic Theory|
|Department:||Strathclyde Business School > Economics|
|Depositing user:||Pure Administrator|
|Date Deposited:||02 Jul 2012 09:15|
|Last modified:||07 Jan 2017 01:14|