Stock-Flow Dynamic Projection

Borrowing from our experience in agent-based computational economic research from `bottom-up', this paper considers economic system as multi-level dynamical system that micro-level agents' interaction leads to structural transition in meso-level, which results in macro-level market dynamics with endogenous fluctuation or even market crashes. By the concept of transition matrix, we develop technique to quantify meso-level structural change induced by micro-level interaction. Then we apply this quantification to propose the method of dynamic projection that delivers out-of-sample forecast of macro-level economic variable from micro-level big data. We testify this method with a data set of financial statements for 4599 firms listed in Tokyo Stock Exchange for the year of 1980 to 2012. The Diebold-Mariano test indicates that the dynamic projection has significantly higher accuracy for one-period-ahead out-of-sample forecast than the benchmark of ARIMA models.