A note on the estimation of linear regression models with Heteroskedastic measurement errors

Daniel Sullivan

No WP-01-23, Working Paper Series from Federal Reserve Bank of Chicago

Abstract: I consider the estimation of linear regression models when the independent variables are measured with errors whose variances differ across observations, a situation that arises, for example, when the explanatory variables in a regression model are estimates of population parameters based on samples of varying sizes. Replacing the error variance that is assumed common to all observations in the standard errors-in-variables estimator by the mean measurement error variance yields a consistent estimator in the case of measurement error heteroskedasticity. However, another estimator, which I call the Heteroskedastic Errors in Variables Estimator (HEIV), is, under standard assumptions, asymptotically more efficient. Simulations show that the efficiency gains are likely to appreciable in practice. In addition, the HEIV estimator, which is equal to the ordinary least squares regression of the dependent variable on the best linear predictor of the true independent variables, is simple to compute with standard regression software.

Keywords: Regression; analysis (search for similar items in EconPapers)
Date: 2001
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-pke
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Citations: View citations in EconPapers (8)

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