×

Using threshold autoregressive models to study dyadic interactions. (English) Zbl 1179.62132

Summary: Considering a dyad as a dynamic system whose current state depends on its past state has allowed researchers to investigate whether and how partners influence each other. Some researchers have also focused on how differences between dyads in their interaction patterns are related to other differences between them. A promising approach in this area is the model that was proposed by Gottman and Murray, which is based on nonlinear coupled difference equations. In this paper, it is shown that their model is a special case of the threshold autoregressive (TAR) model. As a consequence, we can make use of existing knowledge about TAR models with respect to parameter estimation, model alternatives and model selection. We propose a new estimation procedure and perform a simulation study to compare it to the estimation procedure developed by Gottman and Murray. In addition, we include an empirical example based on interaction data of three dyads.

MSC:

62M15 Inference from stochastic processes and spectral analysis
37N99 Applications of dynamical systems
62P15 Applications of statistics to psychology

References:

[1] Bisconti, T., Bergeman, C. S., & Boker, S. M. (2004). Emotional well-being in recently bereaved widows: A dynamical system approach. Journal of Gerontology, Series B: Psychological Sciences and Social Sciences, 59, 158–167.
[2] Carver, C. S., & Scheier, M. F. (1998). On the self-regulation of behavior. New York: Cambridge University Press.
[3] Chan, K. S., & Tong, H. (1990). On likelihood ratio tests for threshold autoregression. Journal of the Royal Statistical Society, 52, 469–476. · Zbl 0706.62078
[4] Chen, C. W. S. (1998). A Bayesian analysis of generalized threshold autoregressive models. Statistical and Probability Letters, 40, 15–22. · Zbl 0933.62084 · doi:10.1016/S0167-7152(98)00077-7
[5] Chow, S.-M., Ferrer, E., & Nesselroade, J. R. (2007). An unscented Kalman filter approach to the estimation of nonlinear dynamic system models. Multivariate Behavioral Research, 42, 283–321.
[6] Clayton, K. (1997). Basic concepts in nonlinear dynamics and chaos. Workshop presented at the society for chaos theory in psychology and the life sciences meeting.
[7] Cook, J., Tyson, R., White, R. R., Gottman, J. M., & Murray, J. (1995). Mathematics of marital conflict: Qualitative dynamic mathematical modeling of marital interaction. Journal of Family Psychology, 9, 110–130. · doi:10.1037/0893-3200.9.2.110
[8] De Gooijer, J. G. (2001). Cross-validation criteria for SETAR model selection. Journal of Time Series Analysis, 22, 267–281. · Zbl 0978.62077 · doi:10.1111/1467-9892.00223
[9] Fan, J., & Yao, Q. (2003). Nonlinear time series: Nonparametric and parametric methods. New York: Springer. · Zbl 1014.62103
[10] Gonzalo, J., & Pitarakis, J.-Y. (2002). Estimation and model selection based inference in single and multiple threshold models. Journal of Econometrics, 110, 319–352. · Zbl 1043.62068 · doi:10.1016/S0304-4076(02)00098-2
[11] Gonzalo, J., & Wolf, M. (2005). Subsampling inference in threshold autoregressive models. Journal of Econometrics, 127, 201–224. · Zbl 1335.62134 · doi:10.1016/j.jeconom.2004.08.004
[12] Gottman, J. M., Coan, J., Carrere, S., & Swanson, C. (1998). Predicting marital happiness and stability from newlywed interactions. Journal of Marriage and the Family, 60, 5–22. · doi:10.2307/353438
[13] Gottman, J. M., Levenson, R. W., Swanson, C., Swanson, K., Tyson, R., & Yoshimoto, D. (2003). Observing gay, lesbian, and heterosexual couple’s relationships: Mathematical modeling of conflict interaction. Journal of Homosexuality, 45, 65–91. · doi:10.1300/J082v45n01_04
[14] Gottman, J. M., McCoy, K., Coan, J., & Collier, H. (1996). The specific affect coding system SPAFF. In J. M. Gottman (Ed.), What predicts divorce? The measures (pp. 1–169). Hillsdale: Lawrence Erlbaum Associates.
[15] Gottman, J. M., Murray, J. D., Swanson, C. C., Tyson, R., & Swanson, K. R. (2002). The mathematics of marriage: Dynamic nonlinear models. Cambridge: MIT Press. · Zbl 1014.91080
[16] Gottman, J. M., Swanson, C. C., & Murray, J. D. (1999). The mathematics of marital conflict: Dynamic mathematical nonlinear modeling of newlywed marital interaction. Journal of Family Psychology, 13, 3–19. · doi:10.1037/0893-3200.13.1.3
[17] Granger, C. W. J., & Andersen, A. P. (1978). An introduction to bilinear time series models. Göttingen: Vandenhoeck und Ruprecht. · Zbl 0379.62074
[18] Granic, I., & Hollenstein, T. (2003). Dynamic system methods for models of developmental psychopathology. Development and Psychopathology, 15, 641–669. · doi:10.1017/S0954579403000324
[19] Guastello, S. J. (1997). Science evolves: An introduction to nonlinear dynamics, psychology, and life sciences. Nonlinear Dynamics, Psychology, and Life Sciences, 1, 1–6. · doi:10.1023/A:1022367709123
[20] Hamaker, E. L., Dolan, C. V., & Molenaar, P. C. M. (2003). ARMA-based SEM when the number of time points T exceeds the number of cases N: Raw data maximum likelihood. Structural Equation Modeling, 10, 352–379. · doi:10.1207/S15328007SEM1003_2
[21] Hansen, B. E. (1997). Inference in TAR models. Studies in Nonlinear Dynamics and Econometrics, 2, 1–14. · Zbl 1078.91558 · doi:10.2202/1558-3708.1024
[22] Kapetanios, G. (2003). Using extraneous information and GMM to estimate threshold parameters in TAR models (U of London Queen Mary Economics Working Paper No. 494). Available at SSRN: http://ssrn.com/abstract=425380 or doi: 10.2139/ssrn.425380 .
[23] Murray, J. D. (2002). Mathematical biology: I. An introduction (3 ed.). New York: Springer. · Zbl 1006.92001
[24] Normand, S.-L. (1999). Tutorial in biostatistics. Meta-analysis: Formulating, evaluating, combining and reporting. Statistics in Medicine, 18, 321–359. · doi:10.1002/(SICI)1097-0258(19990215)18:3<321::AID-SIM28>3.0.CO;2-P
[25] Olthof, T., Kunnen, E. S., & Boom, J. (2000). Simulating mother-child interaction: Exploring two varieties of a non-linear dynamic system approach. Infant and Child Development, 9, 33–60. · doi:10.1002/(SICI)1522-7219(200003)9:1<33::AID-ICD213>3.0.CO;2-6
[26] Politis, D. N. (2003). The impact of bootstrap methods on time series analysis. Statistical Science, 18, 219–230. · Zbl 1332.62340 · doi:10.1214/ss/1063994977
[27] Politis, D. N., & Romano, J. P. (1994). Large sample confidence regions based on subsamples under minimal assumptions. The Annals of Statistics, 22, 2031–2050. · Zbl 0828.62044 · doi:10.1214/aos/1176325770
[28] Schiepek, G. (2003). A dynamic system approach to clinical case formulation. European Journal of Psychological Assessment, 19, 175–184. · doi:10.1027//1015-5759.19.3.175
[29] Schwartz Gottman, J. (2004). The marriage clinic casebook. New York: W.W. Norton and Co.
[30] Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461–464. · Zbl 0379.62005 · doi:10.1214/aos/1176344136
[31] Shoda, Y., Tiernan, S. L., & Mischel, W. (2002). Personality as a dynamic system: Emergence of stability and distinctiveness from intra- and interpersonal interactions. Personality and Social Psychology Review, 6, 316–325. · doi:10.1207/S15327957PSPR0604_06
[32] Stijnen, T. (2000). Tutorial in biostatistics. meta-analysis: Formulating, evaluating, combining and reporting. Statistics in Medicine, 19, 753–761. · doi:10.1002/(SICI)1097-0258(20000315)19:5<759::AID-SIM428>3.0.CO;2-V
[33] Strikholm, B., & Teräsvirta, T. (2006). A sequential procedure for determining the number of regimes in a threshold autoregressive model. Econometrics Journal, 9, 472–491. · Zbl 1106.62091 · doi:10.1111/j.1368-423X.2006.00194.x
[34] Thelen, E., & Smith, L. B. (1994). A dynamic system approach to the development of cognition and action. Cambridge: MIT Press.
[35] Tong, H., & Lim, K. S. (1980). Threshold autoregression, limit cycles and cyclical data. Journal of the Royal Statistical Society, B, 42, 245–292. · Zbl 0473.62081
[36] Tsay, R. S. (1998). Testing and modeling multivariate threshold models. Journal of the American Statistical Association, 93, 1188–1202. · Zbl 1063.62578 · doi:10.2307/2669861
[37] Vallacher, R. R., & Nowak, A. (1997). The emergence of dynamical social psychology. Psychological Inquiry, 8, 73–99. · doi:10.1207/s15327965pli0802_1
[38] Vallacher, R. R., Read, S. J., & Nowak, A. (2002). The dynamical perspective in personality and social psychology. Personality and Social Psychology, 4, 264–273.
[39] Van der Maas, H. L. J., & Molenaar, P. C. M. (1992). Stagewise cognitive development: An application of catastrophe theory. Psychological Review, 99, 395–417. · doi:10.1037/0033-295X.99.3.395
[40] Van der Maas, H. L. J., & Raijmakers, M. E. J. (2000). A phase transition model for mother-child interaction: Comment on Olthof et al., 2000. Infant and Child Development, 9, 75–83. · doi:10.1002/1522-7219(200006)9:2<75::AID-ICD214>3.0.CO;2-Q
[41] Van Geert, P., & Van Dijk, M. (2002). Focus on variability: New tools to study intra-individual variability in developmental data. Infant Behavior and Development, 25, 340–374. · doi:10.1016/S0163-6383(02)00140-6
[42] Wang, L., & McArdle, J. J. (2008). A simulation study comparison of Bayesian estimate with conventional methods for estimating unknown change points. Structural Equation Modeling, 15, 52–74.
[43] Warren, K. (2002). Thresholds and the abstinence violation effect: A nonlinear dynamic model of the behaviors of intellectually disabled sex offenders. Journal of Interpersonal Violence, 17, 1198–1217. · doi:10.1177/088626002237402
[44] Warren, K., Hawkins, R. C., & Sprott, J. C. (2003). Substance abuse as a dynamical disease: Evidence and clinical implications of nonlinearity in a time series of daily alcohol consumption. Addictive Behaviors, 28, 369–374. · doi:10.1016/S0306-4603(01)00234-9
[45] Witkiewitz, K., Van der Maas, J. L., Hufford, M. R., & Marlatt, G. A. (2007). Nonnormality and divergence in posttreatment alcohol use: Reexamining the Project MATCH data another way. Journal of Abnormal Psychology, 116, 378–394. · doi:10.1037/0021-843X.116.2.378
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.