Estimating nonlinear models with multiply imputed data

Catherine Phillips Montalto, Yoonkyung Yuh

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)97-103
Number of pages7
JournalJournal of Financial Counseling and Planning
Volume9
Issue number1
StatePublished - 1998

Keywords

  • Logit
  • Probit
  • Repeated-imputation inference (RII)
  • Survey of consumer finances
  • Tobit

Fingerprint

Dive into the research topics of 'Estimating nonlinear models with multiply imputed data'. Together they form a unique fingerprint.

Cite this