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

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  • Richard A. Ashley
  • Kwok Ping Tsang
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    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. But post-sample model testing requires an often-consequential a priori partitioning of the data into an 'in-sample' period - purportedly utilized only for model specifi- cation/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 less than 100 - as is common in both quarterly data sets and/or in monthly data sets where institutional arrange- ments 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 substantially ameliorates this 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 al- ways 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 and West (2005) study of the causal relationship between macroeconomic fundamentals and the exchange rate.

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    File URL: ftp://repec.econ.vt.edu/Papers/Ashley/Ashley_Tsang_Cross_Sample_Validation_Granger_Causality.pdf
    File Function: First version, 2013
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    Bibliographic Info

    Paper provided by Virginia Polytechnic Institute and State University, Department of Economics in its series Working Papers with number e07-41.

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    Length: 20 pages
    Date of creation: 2013
    Date of revision:
    Handle: RePEc:vpi:wpaper:e07-41

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    Related research

    Keywords: Time Series; Granger-causality; causality; post-sample testing; exchange rates.;

    References

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    1. J. Isaac Miller & Shawn Ni, 2010. "Long-Term Oil Price Forecasts: A New Perspective on Oil and the Macroeconomy," Working Papers, Department of Economics, University of Missouri 1012, Department of Economics, University of Missouri.
    2. Hooker, Mark A., 1996. "What happened to the oil price-macroeconomy relationship?," Journal of Monetary Economics, Elsevier, Elsevier, vol. 38(2), pages 195-213, October.
    3. Richard A. Ashley & Kwok Ping Tsang & Randal J. Verbrugge, 2013. "Frequency Dependence in a Real-Time Monetary Policy Rule," Working Papers, Virginia Polytechnic Institute and State University, Department of Economics e07-43, Virginia Polytechnic Institute and State University, Department of Economics.
    4. Kilian, Lutz, 2006. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," CEPR Discussion Papers, C.E.P.R. Discussion Papers 5994, C.E.P.R. Discussion Papers.
    5. Mork, Knut Anton, 1989. "Oil and Macroeconomy When Prices Go Up and Down: An Extension of Hamilton's Results," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 97(3), pages 740-44, June.
    6. Davis, Steven J. & Haltiwanger, John, 2001. "Sectoral job creation and destruction responses to oil price changes," Journal of Monetary Economics, Elsevier, Elsevier, vol. 48(3), pages 465-512, December.
    7. 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, Virginia Polytechnic Institute and State University, Department of Economics e06-11, Virginia Polytechnic Institute and State University, Department of Economics.
    8. James D. Hamilton, 2000. "What is an Oil Shock?," NBER Working Papers 7755, National Bureau of Economic Research, Inc.
    9. Hamilton, James D, 1983. "Oil and the Macroeconomy since World War II," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 91(2), pages 228-48, April.
    10. Kiseok Lee & Shawn Ni & Ronald A. Ratti, 1995. "Oil Shocks and the Macroeconomy: The Role of Price Variability," The Energy Journal, International Association for Energy Economics, International Association for Energy Economics, vol. 0(Number 4), pages 39-56.
    11. Rebeca Jimenez-Rodriguez & Marcelo Sanchez, 2005. "Oil price shocks and real GDP growth: empirical evidence for some OECD countries," Applied Economics, Taylor & Francis Journals, Taylor & Francis Journals, vol. 37(2), pages 201-228.
    12. Engle, Robert F, 1974. "Band Spectrum Regression," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 15(1), pages 1-11, February.
    13. Hamilton, James D., 1996. "This is what happened to the oil price-macroeconomy relationship," Journal of Monetary Economics, Elsevier, Elsevier, vol. 38(2), pages 215-220, October.
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    Cited by:
    1. Richard A. Ashley & Christopher F. Parmeter, 2013. "Sensitivity Analysis For Inference In 2SLS Estimation With Possibly-Flawes Instruments," Working Papers, Virginia Polytechnic Institute and State University, Department of Economics e07-38, Virginia Polytechnic Institute and State University, Department of Economics.
    2. Richard A. Ashley & Kwok Ping Tsang, 2013. "International Evidence On The Oil Price-Real Output Relationship: Does Persistence Matter?," Working Papers, Virginia Polytechnic Institute and State University, Department of Economics e07-42, Virginia Polytechnic Institute and State University, Department of Economics.

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