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Sy-Miin Chow1, Jiyun Zu2, Kim Shifren3

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This study introduces a dynamic factor model with time-varying parameters to analyze complex multivariate time series. The model refines understanding of how positive and negative affect dynamics change over time in Parkinson

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Area of Science:

  • Psychology
  • Statistics
  • Computational Social Science

Background:

  • Multivariate time series data often exhibit complex dynamics.
  • Traditional models may not capture time-varying properties effectively.
  • Dynamic factor models offer a flexible framework for such data.

Purpose of the Study:

  • To develop and apply a dynamic factor model with time-varying parameters.
  • To analyze daily affect data from individuals with Parkinson's disease.
  • To refine the Dynamic Model of Activation using empirical data.

Main Methods:

  • Constructed a dynamic factor model with vector autoregressive relations.
  • Incorporated time-varying cross-regression parameters at the factor level.
  • Utilized state-space techniques to fit the model to daily affect data.

Main Results:

  • Empirical results partially supported the Dynamic Model of Activation.
  • Demonstrated how time dependencies between positive and negative affects evolve.
  • Identified potential refinements to the existing activation model.

Conclusions:

  • Dynamic factor models with time-varying parameters are valuable for analyzing affect dynamics.
  • The proposed model offers a nuanced understanding of affective changes over time.
  • Findings contribute to the study of psychological dynamics in clinical populations.