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CPS2014 Ma. S.B.P. et al.
The proposed estimation procedure is fairly robust as assessed and
evaluated by the MAPE and APB. The estimation procedure produces low
MAPE over the different series length and has estimates that are robust
to misspecification error compared to VAR(1).
The dimension reduction method of SPCA has allowed the
contribution of the high dimensional inputs to be incorporated in the
estimation process. The embedded methods in the backfitting algorithm
helped in yielding robust estimates and in providing a good predictive
ability for the estimation procedure. The nonparametric regression with
the modified sieve bootstrap method for the bivariate series with
correlated components helped in producing consistent estimates. The
combined nonparametric methods of backfitting embedded with VAR(1)
and GAM plus the residual based bootstrap approach helped in providing
better estimates.

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