Determining causal inference in linear and non-linear time-series using convergent cross mapping: an application of government expenditure and economic growth relation in Mexico 1980-2015

Abstract

This study has two major purposes: (1) to identify causal inference in time-series using Granger causality tests and Convergent Cross Mapping (Sugihara et al., 2012) (2) to investigate the economic growth and government expenditure relation in Mexico. Convergent Cross Mapping (CCM) has shown a high potential to perform causal inference in complex systems and non-linear system and has been used as an alternative approach to Granger causality. One one hand, we show that CCM fails to infer causality direction in linear time-series and in time-series with structural breaks. On the other hand, we demonstrate that Toda-Yamamoto test (Toda and Yamamoto, 1995) successfully detects causal relation in linear systems and systems with structural breaks. Besides, we evaluate the causal relation between government expenditure and economic growth in Mexico then we evaluate the validity of Wagner’s law and the Keynesian view. The empirical results suggests that Wagner’s law holds for Mexico for the period 1980 to 2010.