Symbolic transfer entropy test for causality in longitudinal data

Abstract

In this study, we use multiple-unit symbolic dynamics and transfer entropy to develop a non-parametric Granger causality test procedure for longitudinal data. Monte Carlo simulations show that our test exhibits the correct size and a high power in situations where linear panel data causality tests fail, such as (1) when the linearity assumption does not hold, (2) when the data generating process is heterogeneous across the cross-section units or presents structural breaks, (3) when there are extreme observations in some of the cross-section units, (4) when the process exhibits causal dependence on the conditional variance, or (5) when the analysis involves qualitative data. We illustrate the usefulness of our proposed procedure by analyzing the dynamic causal relationships between public expenditure and GDP, between firm productivity and firm size in US manufacturing sectors, and among sovereign credit ratings, growth, and interest rates.