Panel Data Causal Inference Using a Rigorous Information Flow Analysis for Homogeneous, Independent and Identically Distributed Datasets

Panel data, which consist of observations on many individual units over two or more instances of time, have gradually become an important type of scientific data. Subsequently causal inference for panel data has attracted enormous interest from many fields as well as statistics. In this study, the rigorously formulated information flow analysis for time series, which is very concise in form and has been successfully applied in different disciplines, is generalized to identify the causality from homogeneous and independent identically distributed panel data. The resulting formula bears the same form as that for the former, though the meanings of the symbols differ. An algorithm is then proposed for panel data causality analysis, which has been validated with both linear and nonlinear problems. It has also been put to application to examine the causal relations among economic growth, energy consumption, trade openness, and energy price based on 15 Asian countries. Clearly identified are a strong bidirectional causality between economic growth and energy consumption, and a strong causality from import and export trade to economic growth. Energy price has no direct impact on energy consumption; it, instead, exerts a weak effect on the latter through influencing economic growth.