VAR(1) based models do not always outpredict AR(1) models in typical psychological applications

Psychological Methods

Volumn 23, Issue 4, 2018, Pages 740-756

  Bulteel, Kirsten ^a   Mestdagh, Merijn ^a   Tuerlinckx, Francis ^a   Ceulemans, Eva ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Cross validation; Individual differences; Predictive accuracy; Vector autoregressive modeling; Within person dynamics

Indexed keywords

ARTICLE; CONTROLLED STUDY; ERROR; HUMAN; PREDICTION; PSYCHOLOGICAL MODEL; SIMULATION; VALIDATION PROCESS; INDIVIDUALITY; PROCEDURES; PSYCHOLOGY; STATISTICAL ANALYSIS; STATISTICAL MODEL;

DATA INTERPRETATION, STATISTICAL; HUMANS; INDIVIDUALITY; MODELS, PSYCHOLOGICAL; MODELS, STATISTICAL; PSYCHOLOGY;

EID: 85046785183     PISSN: 1082989X     EISSN: None     Source Type: Journal    
DOI: 10.1037/met0000178     Document Type: Article

References (64)

1. Abegaz, F., & Wit, E. (2013). Sparse time series chain graphical models for reconstructing genetic networks. Biostatistics, 14, 586-599. http://dx.doi.org/10.1093/biostatistics/kxt005
2. Akaike, H. (1974). A new look at statistical model identification. IEEE Transactions on Automatic Control, 19, 716-723. http://dx.doi.org/10.1109/TAC.1974.1100705
3. Babyak, M. A. (2004). What you see may not be what you get: A brief, nontechnical introduction to overfitting in regression-type models. Psychosomatic Medicine, 66, 411-421
4. Bergmeir, C., & Benítez, J. M. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192-213. http://dx.doi.org/10.1016/j.ins.2011.12.028
5. Bolger, N., & Laurenceau, J.-P. (2013). Intensive longitudinal methods: An introduction to diary and experience sampling research. New York, NY: Guilford Press
6. Borsboom, D., & Cramer, A. O. J. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91-121. http://dx.doi.org/10.1146/annurevclinpsy-050212-185608
7. Bos, E. H., Hoenders, R., & de Jonge, P. (2012). Wind direction and mental health: A time-series analysis of weather influences in a patient with anxiety disorder. British Medical Journal Case Reports. Advance online publication. http://dx.doi.org/10.1136/bcr-2012-006300
8. Brandt, P. T., & Williams, J. T. (2007). Multiple time series models. Thousand Oaks, CA: Sage. http://dx.doi.org/10.4135/9781412985215
9. Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16, 199-231. http://dx.doi.org/10.1214/ss/1009213726
10. Bringmann, L. F., Pe, M. L., Vissers, N., Ceulemans, E., Borsboom, D., Vanpaemel, W., Kuppens, P. (2016). Assessing temporal emotion dynamics using networks. Assessment, 23, 425-435. http://dx.doi.org/ 10.1177/1073191116645909
11. Bringmann, L. F., Vissers, N., Wichers, M., Geschwind, N., Kuppens, P., Peeters, F., Tuerlinckx, F. (2013). A network approach to psychopathology: New insights into clinical longitudinal data. PLoS ONE, 8, e60188. http://dx.doi.org/10.1371/journal.pone.0060188
12. Bulteel, K., Tuerlinckx, F., Brose, A., & Ceulemans, E. (2016a). Clustering vector autoregressive models: Capturing qualitative differences in within-person dynamics. Frontiers in Psychology, 7, 1540. http://dx.doi.org/10.3389/fpsyg.2016.01540
13. Bulteel, K., Tuerlinckx, F., Brose, A., & Ceulemans, E. (2016b). Using raw VAR regression coefficients to build networks can be misleading. Mul-tivariate Behavioral Research, 51, 330-344. http://dx.doi.org/10.1080/ 00273171.2016.1150151
14. Burman, P., Chow, E., & Nolan, D. (1994). A cross-validatory method for dependent data. Biometrika, 81, 351-358. http://dx.doi.org/10.1093/ biomet/81.2.351
15. Coifman, K. G., Bonanno, G. A., & Rafaeli, E. (2007). Affect dynamics, bereavement, and resilience to loss. Journal of Happiness Studies, 8, 371-392. http://dx.doi.org/10.1007/s10902-006-9014-5
16. Cools, W., De Fraine, B., Van den Noortgate, W., & Onghena, P. (2009). Multilevel design efficiency in educational effectiveness research. School Effectiveness and School Improvement, 20, 357-373. http://dx.doi.org/10.1080/09243450902850176
17. Douglas, H. E. (2009). Reintroducing prediction to explanation. Philosophy of Science, 76, 444-463. http://dx.doi.org/10.1086/648111
18. Gates, K. M., & Molenaar, P. C. M. (2012). Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. NeuroImage, 63, 310-319. http://dx.doi.org/10.1016/j.neuroimage.2012.06.026
19. Geisser, S. (1975). The predictive sample reuse method with applications. Journal of the American Statistical Association, 70, 320-328. http://dx.doi.org/10.1080/01621459.1975.10479865
20. Geschwind, N., Peeters, F., Drukker, M., van Os, J., & Wichers, M. (2011). Mindfulness training increases momentary positive emotions and reward experience in adults vulnerable to depression: A randomized controlled trial. Journal of Consulting and Clinical Psychology, 79, 618-628. http://dx.doi.org/10.1037/a0024595
21. Hamaker, E. L., Ceulemans, E., Grasman, R. P. P. P., & Tuerlinckx, F. (2015). Modeling affect dynamics: State-of-the-art and future challenges. Emotion Review, 7, 316-322. http://dx.doi.org/10.1177/ 1754073915590619
22. Hamilton, J. D. (1994). Time series analysis. Princeton, NJ: Princeton University Press
23. Harrison, L., Penny, W. D., & Friston, K. (2003). Multivariate autoregressive modeling of fMRI time series. NeuroImage, 19, 1477-1491. http:// dx.doi.org/10.1016/S1053-8119(03)00160-5
24. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. New York, NY: Springer. http://dx.doi.org/10.1007/978-0-387-84858-7
25. Hox, J. J. (2010). Multilevel analysis: Techniques and applications. New York, NY: Routledge
26. Hsu, N.-J., Hung, H.-L., & Chang, Y.-M. (2008). Subset selection for vector autoregressive processes using Lasso. Computational Statistics & Data Analysis, 52, 3645-3657. http://dx.doi.org/10.1016/j.csda.2007.12.004
27. Koval, P., Kuppens, P., Allen, N. B., & Sheeber, L. (2012). Getting stuck in depression: The roles of rumination and emotional inertia. Cognition and Emotion, 26, 1412-1427. http://dx.doi.org/10.1080/02699931.2012.667392
28. Liu, S. (2017). Person-specific versus multilevel autoregressive models: Accuracy in parameter estimates at the population and individual levels. British Journal of Mathematical & Statistical Psychology, 70, 480-498. http://dx.doi.org/10.1111/bmsp.12096
29. Lodewyckx, T., Tuerlinckx, F., Kuppens, P., Allen, N., & Sheeber, L. (2011). A hierarchical state space approach to affective dynamics. Journal of Mathematical Psychology, 55, 68-83. http://dx.doi.org/10.1016/ j.jmp.2010.08.004
30. Lütkepohl, H. (2005). New introduction to multiple time series analysis. Berlin, Germany: Springer. http://dx.doi.org/10.1007/978-3-540-27752-1
31. Mestdagh, M., Verdonck, S., Duisters, K., & Tuerlinckx, F. (2015). Fingerprint resampling: A generic method for efficient resampling. Scientific Reports, 5, 16970. http://dx.doi.org/10.1038/srep16970
32. Molenaar, P. C. M. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement: Interdisciplinary Research and Perspectives, 2, 201-218. http://dx.doi.org/10.1207/s15366359mea0204_1
33. Molenaar, P. C. M., & Campbell, C. G. (2009). The new person-specific paradigm in Psychology. Current Directions in Psychological Science, 18, 112-117. http://dx.doi.org/10.1111/j.1467-8721.2009.01619.x
34. Molenaar, P. C. M., & Lo, L. L. (2016). Alternative forms of Granger causality, heterogeneity, and nonstationarity. In W. Wiedermann & A. von Eye (Eds.), Statistics and causality: Methods for applied empirical research (pp. 205-230). Hoboken, NJ: Wiley. http://dx.doi.org/10.1002/ 9781118947074.ch9
35. Müller, S., Scealy, J. L., & Welsh, A. H. (2013). Model selection in linear mixed models. Statistical Science, 28, 135-167. http://dx.doi.org/10.1214/12-STS410
36. Navarro, D. J., Pitt, M. A., & Myung, I. J. (2004). Assessing the distinguishability of models and the informativeness of data. Cognitive Psychology, 49, 47-84. http://dx.doi.org/10.1016/j.cogpsych.2003.11.001
37. Pe, M. L., Kircanski, K., Thompson, R. J., Bringmann, L. F., Tuerlinckx, F., Mestdagh, M., Gotlib, I. H. (2015). Emotion-network density in major depressive disorder. Clinical Psychological Science, 3, 292-300. http://dx.doi.org/10.1177/2167702614540645
38. Pe, M. L., Koval, P., & Kuppens, P. (2013). Executive well-being: Updating of positive stimuli in working memory is associated with subjective well-being. Cognition, 126, 335-340. http://dx.doi.org/10.1016/j.cognition.2012.10.002
39. Pe, M. L., Raes, F., Koval, P., Brans, K., Verduyn, P., & Kuppens, P. (2013). Interference resolution moderates the impact of rumination and reappraisal on affective experiences in daily life. Cognition and Emotion, 27, 492-501. http://dx.doi.org/10.1080/02699931.2012.719489
40. Pitt, M. A., & Myung, I. J. (2002). When a good fit can be bad. Trends in Cognitive Sciences, 6, 421-425. http://dx.doi.org/10.1016/S1364-6613(02)01964-2
41. Racine, J. (2000). Consistent cross-validatory model-selection for dependent data: Hv-block cross-validation. Journal of Econometrics, 99, 39-61. http://dx.doi.org/10.1016/S0304-4076(00)00030-0
42. Reich, J. W., Zautra, A. J., & Davis, M. (2003). Dimensions of affect relationships: Models and their integrative implications. Review of General Psychology, 7, 66-83. http://dx.doi.org/10.1037/1089-2680.7.1.66
43. Rissanen, J. (1986). Order estimation by accumulated prediction errors. Journal of Applied Probability, 23, 55-61. http://dx.doi.org/10.2307/ 3214342
44. Roebroeck, A., Formisano, E., & Goebel, R. (2005). Mapping directed influence over the brain using Granger causality and fMRI. NeuroImage, 25, 230-242. http://dx.doi.org/10.1016/j.neuroimage.2004.11.017
45. Rosmalen, J. G. M., Wenting, A. M. G., Roest, A. M., de Jonge, P., & Bos, E. H. (2012). Revealing causal heterogeneity using time series analysis of ambulatory assessments: Application to the association between depression and physical activity after myocardial infarction. Psychosomatic Medicine, 74, 377-386. http://dx.doi.org/10.1097/PSY.0b013e3182545d47
46. Rothman, A. J., Levina, E., & Zhu, J. (2010). Sparse multivariate regression with covariance estimation. Journal of Computational and Graphical Statistics, 19, 947-962. http://dx.doi.org/10.1198/jcgs.2010.09188
47. Sbarra, D. A., & Allen, J. J. B. (2009). Decomposing depression: On the prospective and reciprocal dynamics of mood and sleep disturbances. Journal of Abnormal Psychology, 118, 171-182. http://dx.doi.org/10.1037/a0014375
48. Schmiedek, F., Lövdén, M., & Lindenberger, U. (2010). Hundred days of cognitive training enhance broad cognitive abilities in adulthood: Findings from the COGITO study. Frontiers in Aging Neuroscience, 2, 27
49. Schmitz, B., & Skinner, E. (1993). Perceived control, effort, and academic performance: Interindividual, intrainindividual, and multivariate timeseries analysis. Journal of Personality and Social Psychology, 64, 1010-1028. http://dx.doi.org/10.1037/0022-3514.64.6.1010
50. Schuurman, N. K., Ferrer, E., de Boer-Sonnenschein, M., & Hamaker, E. L. (2016). How to compare cross-lagged associations in a multilevel autoregressive model. Psychological Methods, 21, 206-221. http://dx.doi.org/10.1037/met0000062
51. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464. http://dx.doi.org/10.1214/aos/1176344136
52. Shmueli, G. (2010). To explain or to predict? Statistical Science, 25, 289-310. http://dx.doi.org/10.1214/10-STS330
53. Snijders, T. A. B. (1988). On cross-validation for predictor evaluation in time series. In T. K. Dijkstra (Ed.), On model uncertainty and its statistical implications (pp. 56-69). New York, NY: Springer-Verlag. http://dx.doi.org/10.1007/978-3-642-61564-1_4
54. Snippe, E., Bos, E. H., van der Ploeg, K. M., Sanderman, R., Fleer, J., & Schroevers, M. J. (2015). Time-series analysis of daily changes in mindfulness, repetitive thinking, and depressive symptoms during mindfulness-based treatment. Mindfulness, 6, 1053-1062. http://dx.doi.org/10.1007/s12671-014-0354-7
55. Stone, M. (1974). Cross-validation and assessment of statistical predictions. Journal of the Royal Statistical Society Series B: Methodological, 36, 111-147
56. Tibshirani, R. (1996). Shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B: Methodological, 58, 267-288
57. Vaida, F., & Blanchard, S. (2005). Conditional Akaike information for mixed-effects model. Biometrika, 92, 351-370. http://dx.doi.org/10.1093/biomet/92.2.351
58. van der Krieke, L., Emerencia, A. C., Bos, E. H., Rosmalen, J. G. M., Riese, H., Aiello, M., de Jonge, P. (2015). Ecological momentary assessments and automated time series analysis to promote tailored health care: A proof-of-principle study. JMIR Research Protocols, 4, e100. http://dx.doi.org/10.2196/resprot.4000
59. van Gils, A., Burton, C., Bos, E. H., Janssens, K. A. M., Schoevers, R. A., & Rosmalen, J. G. M. (2014). Individual variation in temporal relationships between stress and functional somatic symptoms. Journal of Psychosomatic Research, 77, 34-39. http://dx.doi.org/10.1016/j.jpsychores.2014.04.006
60. Wichers, M. (2014). The dynamic nature of depression: A new micro-level perspective of mental disorder that meets current challenges. Psychological Medicine, 44, 1349-1360. http://dx.doi.org/10.1017/ S0033291713001979
61. Wild, B., Eichler, M., Friederich, H.-C., Hartmann, M., Zipfel, S., & Herzog, W. (2010). A graphical vector autoregressive modelling approach to the analysis of electronic diary data. BMC Medical Research Methodology, 10, 28. http://dx.doi.org/10.1186/1471-2288-10-28
62. Zautra, A. J., Berkhof, J., & Nicolson, N. A. (2002). Changes in affect interrelations as a function of stressful events. Cognition and Emotion, 16, 309-318. http://dx.doi.org/10.1080/02699930143000257
63. Zautra, A. J., Reich, J. W., Davis, M. C., Potter, P. T., & Nicolson, N. A. (2000). The role of stressful events in the relationship between positive and negative affects: Evidence from field and experimental studies. Journal of Personality, 68, 927-951. http://dx.doi.org/10.1111/1467-6494.00121
64. Zheng, Y., Wiebe, R. P., Cleveland, H. H., Molenaar, P. C. M., & Harris, K. S. (2013). An idiographic examination of day-to-day patterns of substance use craving, negative affect, and tobacco use among young adults in recovery. Multivariate Behavioral Research, 48, 241-266. http://dx.doi.org/10.1080/00273171.2013.763012