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Comparing the effectiveness of traditional vs. mechanized identification methods in post-sample forecasting for a macroeconomic Granger causality analysis

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  • Ye, Haichun
  • Ashley, Richard
  • Guerard, John

Abstract

We identify forecasting models using both a traditional, partially judgmental method and the mechanized Autometrics method. We then compare the effectiveness of these two different identification methods for post-sample forecasting, in the context of a relatively large-scale exemplar of macroeconomic post-sample Granger causality testing. This example examines the Granger causal relationships among four macroeconomically important endogenous variables–monthly measures of aggregate income, consumption, consumer prices, and the unemployment rate–embedded in a six-dimensional information set which also includes two interest rates, both of which are taken to be weakly exogenous in this context. We find that models indentified by the traditional method tend to have better post-sample forecasting abilities than analogous models identified using the mechanized method, and that the analysis done using the traditional identification method generates stronger evidence for post-sample Granger causality among the four endogenous variables.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:intfor:v:31:y:2015:i:2:p:488-500
    DOI: 10.1016/j.ijforecast.2014.08.004
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    References listed on IDEAS

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    1. Thomakos, Dimitrios D. & Guerard, John Jr., 2004. "Naive, ARIMA, nonparametric, transfer function and VAR models: A comparison of forecasting performance," International Journal of Forecasting, Elsevier, vol. 20(1), pages 53-67.
    2. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    3. Hendry, David F., 2000. "Econometrics: Alchemy or Science?: Essays in Econometric Methodology," OUP Catalogue, Oxford University Press, number 9780198293545.
    4. Goncalves, Silvia & Kilian, Lutz, 2004. "Bootstrapping autoregressions with conditional heteroskedasticity of unknown form," Journal of Econometrics, Elsevier, vol. 123(1), pages 89-120, November.
    5. Ashley, Richard A. & Patterson, Douglas M., 2010. "Apparent Long Memory In Time Series As An Artifact Of A Time-Varying Mean: Considering Alternatives To The Fractionally Integrated Model," Macroeconomic Dynamics, Cambridge University Press, vol. 14(S1), pages 59-87, May.
    6. 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.
    7. Richard Ashley & Haichun Ye, 2012. "On the Granger causality between median inflation and price dispersion," Applied Economics, Taylor & Francis Journals, vol. 44(32), pages 4221-4238, November.
    8. Ashley, R & Granger, C W J & Schmalensee, R, 1980. "Advertising and Aggregate Consumption: An Analysis of Causality," Econometrica, Econometric Society, vol. 48(5), pages 1149-1167, July.
    9. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    10. Ashley, Richard, 2003. "Statistically significant forecasting improvements: how much out-of-sample data is likely necessary?," International Journal of Forecasting, Elsevier, vol. 19(2), pages 229-239.
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