Examining Nonlinear Changes of Coefficients in Time-Varying Dynamic Factor Models

Master's Thesis


To better investigate complex nonstationary multivariate time series, the current thesis aims to extend earlier work on using time-varying dynamic factor models to represent linear changes of coefficients to nonlinear cases. A method is proposed that uses the extended Kalman smoother (EKS) to track dynamics of both latent factors and time-varying coefficients, the Gaussian maximum likelihood (GML) for point and the bootstrap for standard error (SE) estimation of parameters. The performance of the proposed method was examined via Monte Carlo simulations. Results suggest that (i) the EKS recovered dynamics of latent factors and time-varying coefficients rather faithfully; (ii) the GML and the bootstrap worked well, expect for certain parameters in the dynamic model; (iii) the amplitude of changes of the time-varying coefficient played a crucial role in whether the coefficient can be assumed to be fixed and the accuracy of estimates. Explanations and implications for applied research were also provided.


Attribute NameValues
  • etd-04012008-152657

Author Jiyun Zu
Advisor Ke-Hai Yuan
Contributor Ke-Hai Yuan, Committee Chair
Contributor Steven. M. Boker , Committee Member
Contributor Guangjian Zhang, Committee Member
Contributor Sy-Miin Chow, Committee Member
Degree Level Master's Thesis
Degree Discipline Psychology
Degree Name Master of Arts
Defense Date
  • 2007-10-26

Submission Date 2008-04-01
  • United States of America

  • state-space models

  • Kalman filter

  • time-varying dynamic factor model

  • University of Notre Dame

  • English

Access Rights Open Access
Content License
  • All rights reserved

Departments and Units


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