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Research Articles

Collinearity Issues in Autoregressive Models with Time-Varying Serially Dependent Covariates

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References

  • Adolf, J. K., Voelkle, M. C., Brose, A., & Schmiedek, F. (2017). Capturing context-related change in emotional dynamics via fixed moderated time series analysis. Multivariate Behavioral Research, 52(4), 499–531. https://doi.org/10.1080/00273171.2017.1321978
  • Ariens, S., Ceulemans, E., & Adolf, J. K. (2020). Time series analysis of intensive longitudinal data in psychosomatic research: A methodological overview. Journal of Psychosomatic Research, 137, 110191.
  • Asparouhov, T., Hamaker, E. L., & Muthén, B. (2018). Dynamic structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 25(3), 359–388. https://doi.org/10.1080/10705511.2017.1406803
  • Basinska, B. A., & Gruszczynska, E. (2020). Burnout as a state: random-intercept cross-lagged relationship between exhaustion and disengagement in a 10-day study. Psychology Research and Behavior Management, 13, 267–278. https://doi.org/10.2147/PRBM.S244397
  • Beltz, A. M., & Gates, K. M. (2017). Network mapping with gimme. Multivariate Behavioral Research, 52(6), 789–804.
  • Betz, L. T., Penzel, N., Kambeitz-Ilankovic, L., Rosen, M., Chisholm, K., Stainton, A., Haidl, T. K., Wenzel, J., Bertolino, A., Borgwardt, S., Brambilla, P., Lencer, R., Meisenzahl, E., Ruhrmann, S., Salokangas, R. K. R., Schultze-Lutter, F., Wood, S. J., Upthegrove, R., Koutsouleris, N., & Kambeitz, J, the PRONIA consortium. (2020). General psychopathology links burden of recent life events and psychotic symptoms in a network approach. Npj Schizophrenia, 6(1), 1–8. https://doi.org/10.1038/s41537-020-00129-w
  • Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
  • Bodner, N., Tuerlinckx, F., Bosmans, G., & Ceulemans, E. (2020). Accounting for auto-dependency in binary dyadic time series data: A comparison of model-and permutation-based approaches for testing pairwise associations. British Journal of Mathematical and Statistical Psychology, 74, 86–109.
  • Bosley, H. G., Soyster, P. D., & Fisher, A. J. (2019). Affect dynamics as predictors of symptom severity and treatment response in mood and anxiety disorders: Evidence for specificity. Journal for Person-Oriented Research, 5(2), 101–113.
  • Brandmaier, A. M., Oertzen, T., von Ghisletta, P., Lindenberger, U., & Hertzog, C. (2018). Precision, Reliability, and Effect Size of Slope Variance in Latent Growth Curve Models: Implications for Statistical Power Analysis. Frontiers in Psychology, 9, 294. https://doi.org/10.3389/fpsyg.2018.00294
  • Brans, K., Koval, P., Verduyn, P., Lim, Y. L., & Kuppens, P. (2013). The regulation of negative and positive affect in daily life. Emotion (Washington, D.C.), 13(5), 926–939.
  • Bringmann, L. F., Ferrer, E., Hamaker, E. L., Borsboom, D., & Tuerlinckx, F. (2018). Modeling nonstationary emotion dynamics in dyads using a time-varying vector-autoregressive model. Multivariate Behavioral Research, 53(3), 293–314.
  • Bringmann, L. F., Hamaker, E. L., Vigo, D. E., Aubert, A., Borsboom, D., & Tuerlinckx, F. (2017). Changing dynamics: Time-varying autoregressive models using generalized additive modeling. Psychological Methods, 22(3), 409–425.
  • Bulteel, K., Tuerlinckx, F., Brose, A., & Ceulemans, E. (2016). Using raw var regression coefficients to build networks can be misleading. Multivariate Behavioral Research, 51(2–3), 330–344.
  • Bulteel, K., Tuerlinckx, F., Brose, A., & Ceulemans, E. (2018). Improved insight into and prediction of network dynamics by combining var and dimension reduction. Multivariate Behavioral Research, 53(6), 853–875.
  • Cho, S.-J., Brown-Schmidt, S., & Lee, W. (2018). Autoregressive generalized linear mixed effect models with crossed random effects: An application to intensive binary time series eye-tracking data. Psychometrika, 83(3), 751–771. https://doi.org/10.1007/s11336-018-9604-2
  • Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Routledge.
  • De Boef, S., & Keele, L. (2008). Taking time seriously. American Journal of Political Science, 52(1), 184–200. https://doi.org/10.1111/j.1540-5907.2007.00307.x
  • Degoy, E., & Olmos, R. (2020). Reciprocal relation between health and academic performance in children through autoregressive models. School Psychology (Washington, D.C.), 35(5), 332–342.
  • Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J. R. G., Gruber, B., Lafourcade, B., Leitão, P. J., Münkemüller, T., McClean, C., Osborne, P. E., Reineking, B., Schröder, B., Skidmore, A. K., Zurell, D., & Lautenbach, S. (2013). Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1), 27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x
  • Gistelinck, F., & Loeys, T. (2020). Multilevel autoregressive models for longitudinal dyadic data. TPM: Testing, Psychometrics, Methodology in Applied Psychology, 27(3), 433–452.
  • Groen, R. N., Snippe, E., Bringmann, L. F., Simons, C. J., Hartmann, J. A., Bos, E. H., & Wichers, M. (2019). Capturing the risk of persisting depressive symptoms: A dynamic network investigation of patients’ daily symptom experiences. Psychiatry Research, 271, 640–648.
  • Hamaker, E. L., & Grasman, R. P. (2014). To center or not to center? investigating inertia with a multilevel autoregressive model. Frontiers in Psychology, 5, 1492. https://doi.org/10.3389/fpsyg.2014.01492
  • Hamaker, E., Ceulemans, E., Grasman, R., & Tuerlinckx, F. (2015). Modeling affect dynamics: State of the art and future challenges. Emotion Review, 7(4), 316–322. https://doi.org/10.1177/1754073915590619
  • Hamilton, J. D. (1994). Time series analysis (Vol. 2). Princeton University Press.
  • Harvey, A. C. (1989). Explanatory variables. In Forecasting, structural time series models and the Kalman filter (pp. 365–422). Cambridge University Press.
  • Houben, M., Van Den Noortgate, W., & Kuppens, P. (2015). The relation between short-term emotion dynamics and psychological well-being: A meta-analysis. Psychological Bulletin, 141(4), 901–930.
  • Jebb, A. T., Tay, L., Wang, W., & Huang, Q. (2015). Time series analysis for psychological research: examining and forecasting change. Frontiers in Psychology, 6, 727.
  • Koval, P., & Kuppens, P. (2012). Changing emotion dynamics: individual differences in the effect of anticipatory social stress on emotional inertia. Emotion (Washington, DC), 12(2), 256–267. https://doi.org/10.1037/a0024756
  • Koval, P., Pe, M. L., Meers, K., & Kuppens, P. (2013). Affect dynamics in relation to depressive symptoms: variable, unstable or inert? Emotion (Washington, DC), 13(6), 1132–1141. https://doi.org/10.1037/a0033579
  • Krone, T., Albers, C. J., & Timmerman, M. E. (2017). A comparative simulation study of ar (1) estimators in short time series. Quality & Quantity, 51(1), 1–21. https://doi.org/10.1007/s11135-015-0290-1
  • Krone, T., Albers, C. J., Kuppens, P., & Timmerman, M. E. (2018). A multivariate statistical model for emotion dynamics. Emotion (Washington, DC), 18(5), 739–754.
  • Kuppens, P. (2015). It’s about time: A special section on affect dynamics. Emotion Review, 7(4), 297–300. https://doi.org/10.1177/1754073915590947
  • Kuppens, P., Allen, N. B., & Sheeber, L. B. (2010). Emotional inertia and psychological maladjustment. Psychological Science, 21(7), 984–991.
  • Lafit, G., Adolf, J. K., Dejonckheere, E., Myin-Germeys, I., Viechtbauer, W., & Ceulemans, E. (2021). Selection of the number of participants in intensive longitudinal studies: A user-friendly shiny app and tutorial for performing power analysis in multilevel regression models that account for temporal dependencies. Advances in Methods and Practices in Psychological Science, 4(1), 251524592097873–251524592097824. https://doi.org/10.1177/2515245920978738
  • Lamers, F., Swendsen, J., Cui, L., Husky, M., Johns, J., Zipunnikov, V., & Merikangas, K. R. (2018). Mood reactivity and affective dynamics in mood and anxiety disorders. Journal of Abnormal Psychology, 127(7), 659–669.
  • Leamer, E. E. (1973). Multicollinearity: a Bayesian interpretation. The Review of Economics and Statistics, 55(3), 371–380. https://doi.org/10.2307/1927962
  • Ledoit, O., & Wolf, M. (2003). Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. Journal of Empirical Finance, 10(5), 603–621. https://doi.org/10.1016/S0927-5398(03)00007-0
  • Lütkepohl, H. (2005). New Introduction to multiple time series analysis. Springer.
  • Maeshiro, A. (2000). An illustration of the bias of ols for yt= λ y t-1+ u t. The Journal of Economic Education, 31(1), 76–80.
  • Mason, C. H., & Perreault, W. D. Jr, (1991). Collinearity, power, and interpretation of multiple regression analysis. Journal of Marketing Research, 28(3), 268–280. https://doi.org/10.1177/002224379102800302
  • McClelland, G. H., & Judd, C. M. (1993). Statistical difficulties of detecting interactions and moderator effects. Psychological Bulletin, 114(2), 376–390.
  • McClelland, G. H., Irwin, J. R., Disatnik, D., & Sivan, L. (2017). Multicollinearity is a red herring in the search for moderator variables: A guide to interpreting moderated multiple regression models and a critique of Iacobucci, Schneider, Popovich, and Bakamitsos (2016). Behavior Research Methods, 49(1), 394–402. https://doi.org/10.3758/s13428-016-0785-2
  • Michaelides, M., & Spanos, A. (2020). On modeling heterogeneity in linear models using trend polynomials. Economic Modelling, 85, 74–86. https://doi.org/10.1016/j.econmod.2019.05.008
  • R Core Team. (2021). R: A language and environment for statistical computing [Computer software manual]. https://www.R-project.org/
  • Schuurman, N. K., Houtveen, J. H., & Hamaker, E. L. (2015). Incorporating measurement error in n= 1 psychological autoregressive modeling. Frontiers in Psychology, 6, 1038. https://doi.org/10.3389/fpsyg.2015.01038
  • Shacham, M., & Brauner, N. (1997). Minimizing the effects of collinearity in polynomial regression. Industrial & Engineering Chemistry Research, 36(10), 4405–4412. https://doi.org/10.1021/ie970236k
  • Shieh, G. (2010). On the misconception of multicollinearity in detection of moderating effects: Multicollinearity is not always detrimental. Multivariate Behavioral Research, 45(3), 483–507. https://doi.org/10.1080/00273171.2010.483393
  • Simons, J. S., Simons, R. M., Grimm, K. J., Keith, J. A., & Stoltenberg, S. F. (2021). Affective dynamics among veterans: Associations with distress tolerance and posttraumatic stress symptoms. Emotion, 21(4), 757–771. https://doi.org/10.1037/emo0000745
  • Slinker, B., & Glantz, S. (1985). Multiple regression for physiological data analysis: the problem of multicollinearity. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 249(1), R1–R12. https://doi.org/10.1152/ajpregu.1985.249.1.R1
  • Smith, K. W., & Sasaki, M. S. (1979). Decreasing multicollinearity: A method for models with multiplicative functions. Sociological Methods & Research, 8(1), 35–56. https://doi.org/10.1177/004912417900800102
  • Wang, D., Schneider, S., Schwartz, J. E., & Stone, A. A. (2020). Heightened stress in employed individuals is linked to altered variability and inertia in emotions. Frontiers in Psychology, 11, 1152.
  • Waugh, C. E., Shing, E. Z., Avery, B. M., Jung, Y., Whitlow, C. T., & Maldjian, J. A. (2017). Neural predictors of emotional inertia in daily life. Social Cognitive and Affective Neuroscience, 12(9), 1448–1459.
  • Weisstein, E. W. (n.d.). Geometric series. https://mathworld.wolfram.com/GeometricSeries.html
  • Willis, C. E., & Perlack, R. D. (1978). Multicollinearity: effects, symptoms, and remedies. Journal of the Northeastern Agricultural Economics Council, 7(1), 55–61. https://doi.org/10.1017/S0163548400001989
  • Wolfram Research, Inc. (2020). Mathematica, Version 12.1, Champaign, IL.
  • Wooldridge, J. M. (2012). Basic regression analysis with time series data. In Introductory econometrics: A modern approach (5th ed., pp. 344–380). Cengage learning.

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