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The relationship between the F-test and the Schwarz criterion: Implications for Granger-causality tests

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  • Erdal Atukeren

    (ETH Zurich - KOF Swiss Economic Institute)

Abstract

In applied research, the Schwarz Bayesian Information Criterion (BIC) and the F-test might yield different inferences about the causal relationships being investigated. This paper examines the relationship between the BIC and the F-tests in the context of Granger-causality tests. We calculate the F-test significance levels as a function of the model dimensionality and the sample size that would lead to the same conclusion as the BIC. We illustrate that the BIC would reject the null hypothesis of no-causality less often compared to an F-test conducted at five percent significance level for sample sizes above 50 especially when the chosen model dimensionality is small. Putting the philosophical issues aside, we suggest that the decision to choose between the F-test and the BIC should be made in view of the sample size.

Suggested Citation

  • Erdal Atukeren, 2010. "The relationship between the F-test and the Schwarz criterion: Implications for Granger-causality tests," Economics Bulletin, AccessEcon, vol. 30(1), pages 494-499.
    • Handle: RePEc:ebl:ecbull:eb-10-00017
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    References listed on IDEAS

    as
    1. Erdal Atukeren, 2008. "Christmas cards, Easter bunnies, and Granger-causality," Quality & Quantity: International Journal of Methodology, Springer, vol. 42(6), pages 835-844, December.
    2. Lawrence Raffalovich & Glenn Deane & David Armstrong & Hui-Shien Tsao, 2008. "Model selection procedures in social research: Monte-Carlo simulation results," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(10), pages 1093-1114.
    3. Clive Granger & Yongil Jeon, 2004. "Forecasting Performance of Information Criteria with Many Macro Series," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(10), pages 1227-1240.
    4. Yi, Gang & Judge, George, 1988. "Statistical model selection criteria," Economics Letters, Elsevier, vol. 28(1), pages 47-51.
    5. Nickelsburg, Gerald, 1985. "Small-sample properties of dimensionality statistics for fitting VAR models to aggregate economic data : A Monte Carlo study," Journal of Econometrics, Elsevier, vol. 28(2), pages 183-192, May.
    6. Hsiao, Cheng, 1982. "Autoregressive modeling and causal ordering of economic variables," Journal of Economic Dynamics and Control, Elsevier, vol. 4(1), pages 243-259, November.
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    Cited by:

    1. Dian Lyu & Shruti Naik & David K. Menon & Emmanuel A. Stamatakis, 2022. "Intrinsic brain dynamics in the Default Mode Network predict involuntary fluctuations of visual awareness," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Chainarong Amornbunchornvej & Elena Zheleva & Tanya Berger-Wolf, 2020. "Variable-lag Granger Causality and Transfer Entropy for Time Series Analysis," Papers 2002.00208, arXiv.org, revised Jun 2020.
    3. Chainarong Amornbunchornvej & Elena Zheleva & Tanya Y. Berger-Wolf, 2019. "Variable-lag Granger Causality for Time Series Analysis," Papers 1912.10829, arXiv.org.

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    More about this item

    Keywords

    F-test; Schwarz Bayesian Information Criterion; Model selection; Granger-causality;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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