  The combination of the network theoretic approach with recently available
abundant economic data leads to the development of novel analytic and
computational tools for modelling and forecasting key economic indicators. The
main idea is to introduce a topological component into the analysis, taking
into account consistently all higher-order interactions. We present three basic
methodologies to demonstrate different approaches to harness the resulting
network gain. First, a multiple linear regression optimisation algorithm is
used to generate a relational network between individual components of national
balance of payment accounts. This model describes annual statistics with a high
accuracy and delivers good forecasts for the majority of indicators. Second, an
early-warning mechanism for global financial crises is presented, which
combines network measures with standard economic indicators. From the analysis
of the cross-border portfolio investment network of long-term debt securities,
the proliferation of a wide range of over-the-counter-traded financial
derivative products, such as credit default swaps, can be described in terms of
gross-market values and notional outstanding amounts, which are associated with
increased levels of market interdependence and systemic risk. Third,
considering the flow-network of goods traded between G-20 economies, network
statistics provide better proxies for key economic measures than conventional
indicators. For example, it is shown that a country's gate-keeping potential,
as a measure for local power, projects its annual change of GDP generally far
better than the volume of its imports or exports.
