TY - JOUR
T1 - iVAR
T2 - A program for imputing missing data in multivariate time series using vector autoregressive models
AU - Liu, Siwei
AU - Molenaar, Peter C.M.
N1 - Funding Information:
This study was supported by a Dissertation Award to S.L. from the Society of Multivariate Experimental Psychology, and by National Science Foundation Grant No. 1157220 to P.C.M.M. We thank Matthew Goodwin and Elliot Hedman for providing the empirical data, and Lawrence Lo for assisting with the simulation.
Publisher Copyright:
© 2014, Psychonomic Society, Inc.
PY - 2014/12
Y1 - 2014/12
N2 - 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.
AB - 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.
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U2 - 10.3758/s13428-014-0444-4
DO - 10.3758/s13428-014-0444-4
M3 - Article
C2 - 24515888
AN - SCOPUS:84942121733
VL - 46
SP - 1138
EP - 1148
JO - Behavior Research Methods
JF - Behavior Research Methods
SN - 1554-351X
IS - 4
ER -