A Monte Carlo investigation is used to examine the performance of two commonly used tests for Granger causality for univariate and bivariate nonstationary ARMA (p,q) processes. Tests are applied to raw data, first differences of the raw data, and detrended versions of the series. The results...

The forecasting performance of various multivariate as well as univariate ARIMA models is evaluated in the presence of nonstationarity. The results indicate the importance of identifying the characteristics of the time series by testing for types of nonstationarity. Procedures that permit model...