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Robust Granger Causality/Evidence
Method evidence record

Robust Granger Causality

Robust Granger causality extends the classic Granger causality framework by using bootstrap-based or heteroscedasticity-robust critical values rather than asymptotic chi-squared tables. This makes the test reliable in finite samples and when the data exhibit non-normality, heteroscedasticity, or near-integration, settings where the standard F- or Wald-based test is known to over-reject.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Robust Granger Causality Test
Taxonomic method record · regression-model / econometrics
  • Hacker, R. S., & Hatemi-J, A. (2006). Tests for causality between integrated variables using asymptotic and bootstrap distributions: Theory and application. Applied Economics, 38(13), 1489–1500. · DOI 10.1080/00036840500405763
  • Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438. · DOI 10.2307/1912791
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Claims persisted in the evidence ledger, each with its own assessment.

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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Same method familyCointegration Testmachine-suggested · Relational suggestion, not evidence.Same method familyGranger Causalitymachine-suggested · Relational suggestion, not evidence.Used in the same domainToda-Yamamoto Causalitymachine-suggested · Relational suggestion, not evidence.Same method familyVAR Modelmachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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