Montalto & Yuh (1998). Estimating Nonlinear Models With Multiply Imputed Data, Financial Counseling and Planning, V. 9(1)


Montalto, C. P. & Yuh, Y. (1998). Estimating nonlinear models with multiply imputed data. Financial Counseling and Planning, 9 (1), 97-101.


Estimating Nonlinear Models With Multiply Imputed Data

Catherine Phillips Montalto(1) and Yoonkyung Yuh(2)

Repeated-imputation inference (RII) techniques for estimating nonlinear models with multiply
imputed data are described. RII techniques are used to estimate a logit model using the 1995 Survey
of Consumer Finances. RII techniques use all information available in multiply imputed data and
incorporate estimates of imputation error. The advantage of RII techniques for analysis of multiply
imputed data is that RII techniques produce more efficient estimates and provide a basis for more
valid inference
. Researchers who do not use RII techniques when estimating nonlinear models on
multiply imputed data may incorrectly conclude that some independent variables have statistically
significant effects.



Key Words: Logit, Probit, Repeated-imputation inference (RII), Survey of Consumer Finances, Tobit



Additional resources:
Supplementary logit table


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1. Catherine Phillips Montalto, Assistant Professor, Consumer Sciences Department, The Ohio State University, 1787 Neil Avenue, Columbus,
OH 43210-1295. 614-292-4571. Fax: 614-688-8133. E-mail: montalto.2@osu.edu

2. Yoonkyung Yuh received her Ph.D. from The Ohio State University in September, 1998. E-mail: yoonkyung17@hanmail.net



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