Vector Autoregressions

  • Stock J
  • Watson M
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This article assesses the efficiency of vector autoregressions (VAR) in addressing macroeconometric issues of data description, forecasting, structural inference, and policy analysis. A VAR is an n-equation, n-variable linear model in which each variable is in turn explained by its own lagged values, plus current and past values if the remaining n-1 variables. The simple framework provides a systematic way to capture rich dynamics in multiple series and the statistical toolkit that came with VAR was easy to use and to interpret. According to Sims in 1980, VAR held out the promise of providing a coherent and credible approach to data description, forecasting, structural inference and policy analysis. In connection to this, it is necessary to know that VAR comes in three varieties namely, reduced form, recursive and structural. A reduced form VAR expresses each variable as a linear function of its own past values. On the other hand, a recursive VAR constructs the error terms in each regression equation to be uncorrelated with the error in the receding equations. this is done by including some contemporaneous values as regressors. Lastly, a structural VAR uses economic theory to sort out the contemporaneous links among the variables.

Author-supplied keywords

  • AUTOREGRESSION (Statistics)
  • REGRESSION analysis
  • STOCHASTIC processes

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  • James H Stock

  • Mark Watson

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