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Pitfalls in using Granger causality tests to find an engine of growth

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Author Info

  • Hsiu-Yun Lee
  • Kenneth Lin
  • Jyh-Lin Wu

Abstract

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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File URL: http://www.informaworld.com/openurl?genre=article&doi=10.1080/13504850110088132&magic=repec&7C&7C8674ECAB8BB840C6AD35DC6213A474B5
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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Applied Economics Letters.

Volume (Year): 9 (2002)
Issue (Month): 6 ()
Pages: 411-414

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Handle: RePEc:taf:apeclt:v:9:y:2002:i:6:p:411-414

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Cited by:
  1. Tang, Chor Foon, 2009. "Does causality technique matter to savings-growth nexus in Malaysia?," MPRA Paper 38535, University Library of Munich, Germany.
  2. R. Scott Hacker & Abdulnasser Hatemi-J, 2006. "Tests for causality between integrated variables using asymptotic and bootstrap distributions: theory and application," Applied Economics, Taylor & Francis Journals, vol. 38(13), pages 1489-1500.

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