Variance Decomposition

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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Variance Decomposition or Forecast error variance decomposition indicates the amount of information each variable contributes to the other variables in a Vector autoregression VAR models. Variance decomposition determines how much of the forecast error variance of each of the variable can be explained by exogenous shocks to the other variables. Vector autoregression VAR is an econometric model used to capture the evolution and the interdependencies between multiple time series, generalizing the univariate AR models. All the variables in a VAR are treated symmetrically by including for each variable an equation explaining its evolution based on its own lags and the lags of all the other variables in the model. Based on this feature, Christopher Sims advocates the use of VAR models as a theory-free method to estimate economic relationships, thus being an alternative to the "incredible identification restrictions" in structural models.

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Pagina's: 84, Paperback, Betascript Publishers