Vector Autoregressive Models

In the presented work vector autoregression (VAR) models of finite order are examined. The main part is concerned with stationary VAR processes, whose basic characteristics, various methods of coefficient matrices estimation including consistency conditions are derived. We discuss the point and interval forecasts based on VAR models as well. We also describe integrated processes, principle of cointegration and VEC models which are appropriate modifications of VAR models for cointegration processes. The work also pays attention to Granger's and multi-step causality in the context of VAR models. In the final chapter impulse response analysis and forecast error variance decomposition are presented. Everything is supplemented by illustrative examples on real data