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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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