Cointegration analysis in the presence of outliers

The effects of innovational outliers and additive outliers in cointegrated vector autoregressive models are examined and it is analyzed how outliers can be modelled with dummy variables. A Monte Carlo simulation illustrates that additive outliers are more distortionary than innovational outliers, and misspecified dummies may distort inference on the cointegration rank in finite samples. These findings question the common practice in applied cointegration analyses of including unrestricted dummy variables to account for large residuals. Instead it is suggested to focus on additive outliers, or to test the adequacy of a particular specification of dummies prior to testing for the cointegration rank. The points are illustrated on a UK money demand data set. Copyright Royal Economic Socciety 2004