Robust Estimation of Clustered Multiple Time Series Model with
Structural Change
Resi O. Olivares
A Thesis
Presented to the Faculty
of the University of the Philippines
in Candidacy for the Degree
of Master of Science in Statistics
Recommended for Acceptance
by the
School of Statistics
Adviser: Dr. Erniel B. Barrios
March 2014
ABSTRACT
We postulate a model for a clustered multiple time series where individual and clusters-specific effects
were represented by random components. To induce robustness during episodes of temporary structural
change, we use the forward search and bootstrap methods in the backfitting algorithm to estimate the
parameters. Simulation studies show that the resulting models possess high predictive ability specially in
long time series where structural change usually occur.
Keywords: multiple time series, cluster, structural change, backfitting, additive model, forward search
bootstrap