Regression with imputed covariates: A generalized missing-indicator approach

Valentino Dardanoni (), Salvatore Modica and Franco Peracchi

Journal of Econometrics, 2011, vol. 162, issue 2, 362-368

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)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304-4076(11)00032-7
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Regression with imputed covariates: A generalized missing-indicator approach (2011)
Working Paper: Regression with Imputed Covariates:a Generalized Missing Indicator Approach (2009)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:162:y:2011:i:2:p:362-368

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().