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Credible Granger-Causality Inference with Modest Sample Lengths: A Cross-Sample Validation Approach

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  • Richard A. Ashley

    () (Department of Economics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA)

  • Kwok Ping Tsang

    () (Department of Economics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA)

Abstract

Credible Granger-causality analysis appears to require post-sample inference, as it is well-known that in-sample fit can be a poor guide to actual forecasting effectiveness. However, post-sample model testing requires an often-consequential a priori partitioning of the data into an “in-sample” period – purportedly utilized only for model specification/estimation – and a “post-sample” period, purportedly utilized (only at the end of the analysis) for model validation/testing purposes. This partitioning is usually infeasible, however, with samples of modest length – e.g., T ≤ 150 – as is common in both quarterly data sets and/or in monthly data sets where institutional arrangements vary over time, simply because there is in such cases insufficient data available to credibly accomplish both purposes separately. A cross-sample validation (CSV) testing procedure is proposed below which both eliminates the aforementioned a priori partitioning and which also substantially ameliorates this power versus credibility predicament – preserving most of the power of in-sample testing (by utilizing all of the sample data in the test), while also retaining most of the credibility of post-sample testing (by always basing model forecasts on data not utilized in estimating that particular model’s coefficients). Simulations show that the price paid, in terms of power relative to the in-sample Granger-causality F test, is manageable. An illustrative application is given, to a re-analysis of the Engel andWest [1] study of the causal relationship between macroeconomic fundamentals and the exchange rate; several of their conclusions are changed by our analysis.

Suggested Citation

  • Richard A. Ashley & Kwok Ping Tsang, 2014. "Credible Granger-Causality Inference with Modest Sample Lengths: A Cross-Sample Validation Approach," Econometrics, MDPI, Open Access Journal, vol. 2(1), pages 1-20, March.
  • Handle: RePEc:gam:jecnmx:v:2:y:2014:i:1:p:72-91:d:34391
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    References listed on IDEAS

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    1. Hamilton, James D., 1996. "This is what happened to the oil price-macroeconomy relationship," Journal of Monetary Economics, Elsevier, vol. 38(2), pages 215-220, October.
    2. Davis, Steven J. & Haltiwanger, John, 2001. "Sectoral job creation and destruction responses to oil price changes," Journal of Monetary Economics, Elsevier, vol. 48(3), pages 465-512, December.
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    5. Richard A. Ashley & Randall J. Verbrugge., 2006. "Mis-Specification in Phillips Curve Regressions: Quantifying Frequency Dependence in This Relationship While Allowing for Feedback," Working Papers e06-11, Virginia Polytechnic Institute and State University, Department of Economics.
    6. Richard Ashley & Kwok Ping Tsang & Randal J. Verbrugge, 2010. "Frequency Dependence in a Real-Time Monetary Policy Rule," Working Papers e07-21, Virginia Polytechnic Institute and State University, Department of Economics.
    7. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, pages 291-311.
    8. Hamilton, James D, 1983. "Oil and the Macroeconomy since World War II," Journal of Political Economy, University of Chicago Press, vol. 91(2), pages 228-248, April.
    9. Diks, Cees & Panchenko, Valentyn, 2006. "A new statistic and practical guidelines for nonparametric Granger causality testing," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1647-1669.
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. April Reading List
      by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2014-04-01 20:43:00

    Citations

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    Cited by:

    1. Richard A. Ashley & Kwok Ping Tsang, 2013. "International Evidence On The Oil Price-Real Output Relationship: Does Persistence Matter?," Working Papers e07-42, Virginia Polytechnic Institute and State University, Department of Economics.
    2. David I. Harvey & Stephen J. Leybourne & Emily J. Whitehouse, "undated". "Forecast evaluation tests and negative long-run variance estimates in small samples," Discussion Papers 17/03, University of Nottingham, Granger Centre for Time Series Econometrics.
    3. Richard A. Ashley & Christopher F. Parmeter, 2013. "Sensitivity Analysis For Inference In 2SLS Estimation With Possibly-Flawes Instruments," Working Papers e07-38, Virginia Polytechnic Institute and State University, Department of Economics.
    4. repec:kap:fmktpm:v:31:y:2017:i:4:d:10.1007_s11408-017-0299-7 is not listed on IDEAS
    5. repec:eee:intfor:v:33:y:2017:i:4:p:833-847 is not listed on IDEAS
    6. Ye, Haichun & Ashley, Richard & Guerard, John, 2015. "Comparing the effectiveness of traditional vs. mechanized identification methods in post-sample forecasting for a macroeconomic Granger causality analysis," International Journal of Forecasting, Elsevier, vol. 31(2), pages 488-500.

    More about this item

    Keywords

    time series; Granger-causality; causality; post-sample testing; exchange rates;

    JEL classification:

    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies
    • C - Mathematical and Quantitative Methods
    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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