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A Systematic Study into the Factors that Affect the Predictive Accuracy of Multilevel VAR(1) Models

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  • Ginette Lafit

    (KU Leuven – University of Leuven)

  • Kristof Meers

    (KU Leuven – University of Leuven)

  • Eva Ceulemans

    (KU Leuven – University of Leuven)

Abstract

The use of multilevel VAR(1) models to unravel within-individual process dynamics is gaining momentum in psychological research. These models accommodate the structure of intensive longitudinal datasets in which repeated measurements are nested within individuals. They estimate within-individual auto- and cross-regressive relationships while incorporating and using information about the distributions of these effects across individuals. An important quality feature of the obtained estimates pertains to how well they generalize to unseen data. Bulteel and colleagues (Psychol Methods 23(4):740–756, 2018a) showed that this feature can be assessed through a cross-validation approach, yielding a predictive accuracy measure. In this article, we follow up on their results, by performing three simulation studies that allow to systematically study five factors that likely affect the predictive accuracy of multilevel VAR(1) models: (i) the number of measurement occasions per person, (ii) the number of persons, (iii) the number of variables, (iv) the contemporaneous collinearity between the variables, and (v) the distributional shape of the individual differences in the VAR(1) parameters (i.e., normal versus multimodal distributions). Simulation results show that pooling information across individuals and using multilevel techniques prevent overfitting. Also, we show that when variables are expected to show strong contemporaneous correlations, performing multilevel VAR(1) in a reduced variable space can be useful. Furthermore, results reveal that multilevel VAR(1) models with random effects have a better predictive performance than person-specific VAR(1) models when the sample includes groups of individuals that share similar dynamics.

Suggested Citation

  • Ginette Lafit & Kristof Meers & Eva Ceulemans, 2022. "A Systematic Study into the Factors that Affect the Predictive Accuracy of Multilevel VAR(1) Models," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 432-476, June.
    • Handle: RePEc:spr:psycho:v:87:y:2022:i:2:d:10.1007_s11336-021-09803-z
    DOI: 10.1007/s11336-021-09803-z
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    References listed on IDEAS

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    1. Henk Kiers & Jos Berge, 1994. "The Harris-Kaiser independent cluster rotation as a method for rotation to simple component weights," Psychometrika, Springer;The Psychometric Society, vol. 59(1), pages 81-90, March.
    2. Tanja Krone & Casper J. Albers & Marieke E. Timmerman, 2017. "A comparative simulation study of AR(1) estimators in short time series," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(1), pages 1-21, January.
    3. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    4. John Horn, 1965. "A rationale and test for the number of factors in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 30(2), pages 179-185, June.
    5. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    6. Eva Ceulemans & Iven Mechelen, 2005. "Hierarchical classes models for three-way three-mode binary data: interrelations and model selection," Psychometrika, Springer;The Psychometric Society, vol. 70(3), pages 461-480, September.
    7. Jan Schepers & Eva Ceulemans & Iven Mechelen, 2008. "Selecting Among Multi-Mode Partitioning Models of Different Complexities: A Comparison of Four Model Selection Criteria," Journal of Classification, Springer;The Classification Society, vol. 25(1), pages 67-85, June.
    8. Marieke Timmerman & Henk Kiers, 2003. "Four simultaneous component models for the analysis of multivariate time series from more than one subject to model intraindividual and interindividual differences," Psychometrika, Springer;The Psychometric Society, vol. 68(1), pages 105-121, March.
    9. Henk Kiers & Age Smilde, 2007. "A comparison of various methods for multivariate regression with highly collinear variables," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 16(2), pages 193-228, August.
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