Maybe you would like to check this link - it expands on what I believe you are looking for. The variance in the forecast error of all other variables is completely explained by the variable alone, i.e, the orthogonal shocks to other variables in the system do not increase the variance of your forecast error. Variance decomposition shows how much a shock to one variable impacts the (variance of the) forecast error of a different one - in your case, 50% of the variance in the forecast error of GBP seems to be explained by a unit shock in EUR. The IR of GBP to EUR shows a different pattern - a shock to EUR causes GBP to go down in the near future, but the effect of such shock is mean reverting to 0. Similarly, when GBP goes up by 1 unit of measurement, EUR goes down by about 1/2 on the next period, but the impact of a shock on GBP today on future EUR goes to 0 fast. The nature of these networks and their implied rankings depend on the choice decomposition method. Chan-Lau Diebold and Yilmaz (2015) recently introduced variance decomposition networks as tools for quantifying and ranking the systemic risk of individual firms. (Typically to be expected in stationary VAR models - think of the stationary AR definition.) Variance Decomposition Networks-Mr.Jorge A. It has been widely used in strategy analysis since the heated debate in the 1990s on the relative size of corporate effects vis-à-vis industry and business unit effects. There are two forms of classical decomposition: an additive decomposition and a multiplicative decomposition. Variance decomposition denotes a variety of techniques to decompose the variance of an interested dependent variable into different sources or classes of effects. Factor analytic methods have, for instance, been used extensively in. For example, factor analysis or principal components are tools that are in widespread use.
#HOW TO GET FORECAST VARIANCE DECOMPOSITION FROM EVIEWS 10 SERIES#
It is a relatively simple procedure, and forms the starting point for most other methods of time series decomposition. Variance decomposition is a classical statistical method in multivariate analysis for uncovering simplifying structures in a large set of variables (for example, Anderson, 2003). The decay in the plot illustrates that, as time passes, the effects of a shock in EUR today decay to 0. The classical decomposition method originated in the 1920s. At the initial period, a positive shock on EUR will obviously lead the EUR to go up by the shock amount - thus the initial value of one. In your first graph you plot the impulse-response of EUR to EUR. The data are found in the appendix to Lutkepohl (1991). The VAR(3) model is estimated using investment, durable consumption, and consumption expenditures. In this example, a VAR model is estimated and forecast. Impulse response plots represent what they are named after - the response of a variable given an impulse in another variable. Example 10.1: VAR Estimation and Variance Decomposition.