Pitfalls in using Granger causality tests to find an engine of growth
This paper discusses the reliability of using a Granger causality test to find an engine of growth. The paper first focuses on growth models' cointegration implications since causality must exist in an error-correction model. As a complementary, Monte Carlo experiments with independently generated I(1) variables also indicate a significant probability for rejecting the Granger non-causality null. Given the persistency and cointegration of variables in growth models, rejecting the non-causality null may reflect a spurious causal relationship, rather than confirm a theoretical causality.
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Volume (Year): 9 (2002)
Issue (Month): 6 ()
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