larger than zero (in absolute value). Non-parametric test The above linear methods are appropriate for testing Granger causality in the mean. However they are not able to detect Granger causality in higher moments, e.g., in the variance. Non-parametric tests for Granger causality are designed to address this problem. The definition of Granger causality in these tests is general and does not involve any modelling assumptions, such as a linear autoregressive model. The non-parametric tests for Granger causality can be used as diagnostic tools to build better parametric models including higher order moments and/or non-linearity. Limitations As its name implies, Granger causality is not necessarily true causality. In fact, the Granger-causality tests fulfill only the Humean definition of causality that identifies the cause-effect relations with constant conjunctions. If both X and Y are driven by a common third process with different lags, one might still fail to reject the alternative hypothesis of Granger causality. Yet, manipulation of one of the variables would not change the other. Indeed, the Granger-causality tests are designed to handle pairs of variables, and may produce misleading results when the true relationship involves three or more variables. Having said this, it has been argued that given a probabilistic view of causation, Granger causality can be considered true causality in that sense, especially when Reichenbach's "screening off" notion of probabilistic causation is taken into account. Other possible sources of misguiding test results are: (1) not frequent enough or too frequent sampling, (2) nonlinear causal relationship, (3) time series nonstationarity and nonlinearity and (4) existence of rational expectations. A similar test involving more variables can be applied with vector autoregression. Recently a fundamental mathematical study of the mechanism underlying the Granger method has been provided. By making use exclusively of mathematical tools (Fourier transformation and differential calculus), it has been found that not even the most basic requirement underlying any possible definition of causality is met by the Granger causality test: any definition of causality should refer to the prediction of the future from the past; instead by inverting the time series it can be shown that Granger allows one to ”predict” the past from the future as well. Extensions A method for Granger causality has been developed that is not sensitive to deviations from the assumption that the error term is normally distributed. This method is especially useful in financial economics, since many financial variables are non-normally distributed.