Recent Developments in Modelling Nonstationary Vector Autoregressions
综述了非平稳向量自回归模型的最新进展，包括模型选择、因果检验、脉冲响应等，并通过英国股票、股息和利率数据演示，帮助应用研究者理解变量间关系。
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.