Robust estimation of clustered multiple time series model with structural change - Olivares, Resi O. (School of Statistics, University of the Philippines Diliman, 2014)
Abstract (We postulate a model for 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 ling time series where structural change usually occur).