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The combination of the network theoretic approach with recently availableabundant economic data leads to the development of novel analytic andcomputational tools for modelling and forecasting key economic indicators. Themain idea is to introduce a topological component into the analysis, takinginto account consistently all higher-order interactions. We present three basicmethodologies to demonstrate different approaches to harness the resultingnetwork gain. First, a multiple linear regression optimisation algorithm isused to generate a relational network between individual components of nationalbalance of payment accounts. This model describes annual statistics with a highaccuracy and delivers good forecasts for the majority of indicators. Second, anearly-warning mechanism for global financial crises is presented, whichcombines network measures with standard economic indicators. From the analysisof the cross-border portfolio investment network of long-term debt securities,the proliferation of a wide range of over-the-counter-traded financialderivative products, such as credit default swaps, can be described in terms ofgross-market values and notional outstanding amounts, which are associated withincreased levels of market interdependence and systemic risk. Third,considering the flow-network of goods traded between G-20 economies, networkstatistics provide better proxies for key economic measures than conventionalindicators. 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 farbetter than the volume of its imports or exports.

Netconomics: Novel Forecasting Techniques from the Combination of Big Data, Network Science and Economics