iVAR: a program for imputing missing data in multivariate time series using vector autoregressive models

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Abstract

This article introduces iVAR, an R program for imputing missing data in multivariate time series on the basis of vector autoregressive (VAR) models. We conducted a simulation study to compare iVAR with three methods for handling missing data: listwise deletion, imputation with sample means and variances, and multiple imputation ignoring time dependency. The results showed that iVAR produces better estimates for the cross-lagged coefficients than do the other three methods. We demonstrate the use of iVAR with an empirical example of time series electrodermal activity data and discuss the advantages and limitations of the program.

Original languageEnglish (US)
Pages (from-to)1138-1148
Number of pages11
JournalBehavior research methods
Volume46
Issue number4
DOIs
StatePublished - Dec 1 2014

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Psychology (miscellaneous)
  • Psychology(all)

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