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

Rubi Tonantzin Guitiérrez Villanueva ()
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Rubi Tonantzin Guitiérrez Villanueva: Division of Economics, CIDE, Postal: Carretera México-Toluca 3655, México, D.F. 01210, México

No TESG 008, Graduate theses (Spanish) from CIDE, División de Economía

Abstract: This study has two major purposes: (1) to identify casual inference in time-series using Granger causality tests and Convergent Cross Mapping (Sugihara et. al., 2012) and (2) to investigate the economic growth and government expenditure relation in Mexico. Convergent Cross Mapping (CCM) has shown a high potential to perform casual inference in complex systems and non-linear system and has been used as an alternative approach to Granger causality. On the other 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 demostrate that Toda-Yamamoto test (Toda and Yamamoto, 1995) succesfully detects causal relation 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.

Keywords: Causality; Convergent Cross Mapping; Granger Causality Tests; Government Expenditure; Economic Growth; Mexico. (search for similar items in EconPapers)
JEL-codes: C12 C15 C18 C22 (search for similar items in EconPapers)
Pages: 80 pages
Date: 2016-06
New Economics Papers: this item is included in nep-ecm
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