Regression with Imputed Covariates:a Generalized Missing Indicator Approach

Valentino Dardanoni (), Salvatore Modica () and Franco Peracchi

No 150, CEIS Research Paper from Tor Vergata University, CEIS

Abstract: A common problem in applied regression analysis is that covariate values may be missing for some observations but imputed values may be available. This situation generates a trade-off between bias and precision: the complete cases are often disarmingly few, but replacing the missing observations with the imputed values to gain precision may lead to bias. In this paper we formalize this trade-off by showing that one can augment the regression model with a set of auxiliary variables so as to obtain, under weak assumptions about the imputations, the same unbiased estimator of the parameters of interest as complete-case analysis. Given this augmented model, the bias-precision trade-off may then be tackled by either model reduction procedures or model averaging methods. We illustrate our approach by considering the problem of estimating the relation between income and the body mass index (BMI) using survey data affected by item non-response, where the missing values on the main covariates are filled in by imputations.

Keywords: Missing covariates; Imputations; Bias-precision trade-off; Model reduction; Model averaging; BMI and income. (search for similar items in EconPapers)
JEL-codes: C12 C13 C19 (search for similar items in EconPapers)
Pages: 13 pages
Date: 2009-10-08, Revised 2009-10-08
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (10)

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Related works:
Journal Article: Regression with imputed covariates: A generalized missing-indicator approach (2011)
Working Paper: Regression with imputed covariates: A generalized missing-indicator approach (2011)
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