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- Title
iVAR: A program for imputing missing data in multivariate time series using vector autoregressive models.
- Authors
Liu, Siwei; Molenaar, Peter
- 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.
- Subjects
AUTOREGRESSIVE models; AUTOREGRESSION (Statistics); TIME series analysis; MATHEMATICAL statistics; MISSING data (Statistics)
- Publication
Behavior Research Methods, 2014, Vol 46, Issue 4, p1138
- ISSN
1554-351X
- Publication type
Article
- DOI
10.3758/s13428-014-0444-4