Two Decades of Vector Autoregression (VAR) Modeling

A vector autoregression (VAR) is defined as a vector of endogenous variables regressed against its own lags. VARs therefore are considered part of a general class of simultaneous equations models. By construction, VAR analysis allows us to examine over time the dynamic impacts of innovations to variables on others. The following is a survey of the literature on vector autoregressions (VARs) in the last twenty years since it was first used for policy analysis by Christopher Sims [1980]. Initially, imposing a recursive structure on VAR disturbances had led to criticism that VAR modeling is atheoretical. In tha last decade, however, a number of authors have attempted to remedy the problem by introducing new structural identification techniques. This has enhanced the ability of VARs to model dynamic economic relationships. VAR studies have been used primarily to identify the impacts of aggregate demand and supply shocks on aggregate output, as well as to identify the channels and impacts of monetary policy. The frontiers of current VAR research focus on open economy extensions, as well as on improving lag selection and estimation.