  While data science is battling to extract information from the enormous
explosion of data, many estimators and algorithms are being developed for
better prediction. Researchers and data scientists often introduce new methods
and evaluate them based on various aspects of data. However, studies on the
impact of/on a model with multiple response variables are limited. This study
compares some newly-developed (envelope) and well-established (PLS, PCR)
prediction methods based on real data and simulated data specifically designed
by varying properties such as multicollinearity, the correlation between
multiple responses and position of relevant principal components of predictors.
This study aims to give some insight into these methods and help the researcher
to understand and use them in further studies.
