  This paper selects the NARX neural network as the method through literature
review, and constructs specific NARX neural networks under application
scenarios involving macroeconomic forecasting, national goal setting and global
competitiveness assessment. Through case studies on China, US and Eurozone,
this study explores how those limited & partial exogenous inputs or abundant &
comprehensive exogenous inputs, a small set of most relevant exogenous inputs
or a large set of exogenous inputs covering all major aspects of the macro
economy, whole area related exogenous inputs or both whole area and subdivision
area related exogenous inputs specifically affect the forecasting performance
of NARX neural networks for specific macroeconomic indicators or indices.
Through the case study on Russia this paper explores how the limited & most
relevant exogenous inputs set or the abundant & comprehensive exogenous inputs
set specifically influences the prediction performance of those specific NARX
neural networks for national goal setting. Finally, comparative studies on the
application of NARX neural networks for the forecasts of Global Competitiveness
Indices (GCIs) of various economies are conducted, in order to explore whether
the specific NARX neural network trained on the basis of the GCI related data
of some economies can make sufficiently accurate predictions about GCIs of
other economies, and whether the specific NARX neural network trained on the
basis of the data of some type of economies can give more accurate predictions
about GCIs of the same type of economies than those of different type of
economies. Based on all of the above successful application, this paper
provides policy recommendations on applying fully trained NARX neural networks
that are assessed as qualified to assist or even replace the deductive and
inductive abilities of the human brain in a variety of appropriate tasks.
