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

Siwei Liu1, Peter C M Molenaar

  • 1Human Development and Family Studies, Department of Human Ecology, University of California, Davis, Davis, CA, 95616, USA, sweliu@ucdavis.edu.

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This summary is machine-generated.

iVAR, a new R program, accurately imputes missing data in multivariate time series using vector autoregressive (VAR) models. It outperforms other methods for estimating time-dependent relationships, improving time series analysis.

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Area of Science:

  • Statistics
  • Computational Statistics
  • Time Series Analysis

Background:

  • Missing data is a common challenge in multivariate time series analysis.
  • Traditional methods like listwise deletion or mean imputation can introduce bias and reduce statistical power.
  • Existing multiple imputation methods may not adequately account for the temporal dependencies inherent in time series data.

Purpose of the Study:

  • To introduce iVAR, an R package designed for imputing missing values in multivariate time series data.
  • To evaluate the performance of iVAR against standard missing data handling techniques.
  • To provide a robust tool for researchers working with time series data.

Main Methods:

  • Development of the iVAR R package utilizing vector autoregressive (VAR) models for imputation.
  • A simulation study comparing iVAR with listwise deletion, sample mean/variance imputation, and time-ignoring multiple imputation.
  • Application of iVAR to an empirical dataset of time series electrodermal activity.

Main Results:

  • iVAR demonstrated superior performance in estimating cross-lagged coefficients compared to the other methods.
  • The simulation results indicate that iVAR provides more accurate parameter estimates for time series data with missing values.
  • Empirical application highlights the practical utility of iVAR in real-world time series datasets.

Conclusions:

  • iVAR offers a statistically sound and effective approach for handling missing data in multivariate time series.
  • The program enhances the accuracy of analyzing time-dependent relationships within datasets.
  • iVAR is a valuable addition to the toolkit for time series researchers, with discussed advantages and limitations.