Recent Developments in Modelling Nonstationary Vector Autoregressions

In this paper we review some recent developments in the modelling of nonstationary vector autoregressions (VARs) which we feel have great potential for furthering applied researchers understanding of the relationships linking the variables making up a VAR. The developments surveyed are the use of model determination criteria in selecting lag length, trend order and cointegrating rank, causality testing in vector error correction models, FM-VAR estimation of levels VARS, common trends and cycles analysis, permanent and transitory decompositions, impulse response asymptotics, and the links between cointegrated VARs and structural models. The techniques are illustrated by applications to the modelling of U.K. equities, dividends and interest rates.