COMPUTATIONAL SIMULATION OF INSTITUTIONAL-INVESTMENT DYNAMICS USING PANEL VECTOR AUTOREGRESSION

Abstract

This study develops a computational framework for simulating dynamic interactions between institutional quality indicators and foreign direct investment using a panel Vector Autoregression model applied to a multi-country dataset. The work emphasizes the algorithmic structure of the modeling pipeline, including preprocessing of heterogeneous panel time series, numerical stationarity diagnostics and cointegration testing. Impulse-response simulations are used to examine system behavior following institutional shocks, illustrating the dynamic propagation of disturbances in a high-dimensional environment. Although the empirical application concerns institutional governance, the contribution of this study lies primarily in its computational architecture, numerical considerations, and reproducible design of a data-driven simulation environment. The presented framework demonstrates how computational finance and applied computer science can integrate econometric modeling to analyze complex, interdependent systems.