Granger causality is a statistical concept used to determine if one time series can predict another. It is based on the idea that if a variable X Granger-causes another variable Y, then past values of X should provide information about future values of Y. This does not imply a direct cause-and-effect relationship but rather a predictive relationship based on historical data. To test for Granger causality, researchers typically use regression analysis. They compare the predictive power of a model that includes past values of both X and Y against a model that includes only past values of Y. If the first model performs significantly better, X is said to Granger-cause Y.