Imputed welfare estimates in regression analysis
The authors discuss the use of imputed data in regression analysis, in particular the use of highly disaggregated welfare indicators (from so-called"poverty maps"). They show that such indicators can be used both as explanatory variables on the right-hand side and as the phenomenon to explain on the left-hand side. The authors try out practical ways of adjusting standard errors of the regression coefficients to reflect the error introduced by using imputed, rather than actual, welfare indicators. These are illustrated by regression experiments based on data from Ecuador. For regressions with imputed variables on the left-hand side, the authors argue that essentially the same aggregate relationships would be found with either actual or imputed variables. They address the methodological question of how to interpret aggregate relationships found in such regressions.
|Date of creation:||01 May 2004|
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- Murphy, Kevin M & Topel, Robert H, 1985.
"Estimation and Inference in Two-Step Econometric Models,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 3(4), pages 370-79, October.
- Murphy, Kevin M & Topel, Robert H, 2002. "Estimation and Inference in Two-Step Econometric Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 88-97, January.
- Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
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