Vector Autoregression and Vector Error-Correction Models

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CHAPTER 5 Vector Autoregression and Vector Error-Correction Models Vector Autoregression (VAR) was introduced by Sims (1980) as a technique that could be used by macroeconomists to characterize the joint dynamic behavior of a collection of varia-bles without requiring strong restrictions of the kind needed to identify underlying structural parameters. It has become a prevalent method of time-series modeling. Although estimating the equations of a VAR does not require strong identification as-sumptions, some of the most useful applications of the estimates, such as calculating impulse-response functions (IRFs) or variance decompositions do require identifying restrictions.

5.1 Forecasting and Granger Causality in a VAR In order to identify structural shocks and their dynamic effects we must make additional identification assumptions. However, a simple VAR system such as (5.1) can be used for two important econometric tasks without making any additional assumptions. We can use (5.1) as

  Granger causality, Granger, Causality