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General-to-Specific Model Selection Procedures for Structural Vector Autoregressions


  • Hans-Martin Krolzig

    () (Department of Economics, and Nuffield College, Oxford University)


Structural vector autoregressive (SVAR) models have emerged as a dominant research strategy in empirical macroeconomics, but suffer from the large number of parameters employed and the resulting estimation uncertainty associated with their impulse responses. In this paper we propose general-to-specific model selection procedures to overcome these limitations. After showing that single-equation procedures are efficient for the reduction of the SVAR, but generally not for the reduction of its reduced form, the proposed reduction procedure is computer-automated using PcGets and its small-sample properties are evaluated in a realistic Monte Carlo experiment. The model selection procedure is shown to recover the DGP specification from a large unrestricted SVAR model with controlled size and power. The impulse responses generated by the selected SVAR are compared to those of the unrestricted and reduced VAR and found to be more precise and accurate. The proposed reduction strategy is then applied to the US monetary system considered by Christiano, Eichenbaum and Evans (1996). Although the selection process is hampered by the misspecification of the unrestricted VAR, the results are consistent with the Monte Carlo and question the validity of the impulses responses generated by the full system.

Suggested Citation

  • Hans-Martin Krolzig, 2003. "General-to-Specific Model Selection Procedures for Structural Vector Autoregressions," Economics Papers 2003-W15, Economics Group, Nuffield College, University of Oxford.
  • Handle: RePEc:nuf:econwp:0315

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    References listed on IDEAS

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    5. David F. Hendry & Hans-Martin Krolzig, 2005. "The Properties of Automatic "GETS" Modelling," Economic Journal, Royal Economic Society, vol. 115(502), pages 32-61, March.
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    8. Krolzig, Hans-Martin & Peter Flaschel, 2003. "Wage and Price Phillips Curves," Royal Economic Society Annual Conference 2003 128, Royal Economic Society.
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    Cited by:

    1. Allen, P. Geoffrey & Morzuch, Bernard J., 2006. "Twenty-five years of progress, problems, and conflicting evidence in econometric forecasting. What about the next 25 years?," International Journal of Forecasting, Elsevier, vol. 22(3), pages 475-492.
    2. Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011. "Evaluating Automatic Model Selection," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-33, February.
    3. David F. Hendry & Hans-Martin Krolzig, 2003. "Sub-sample Model Selection Procedures in Gets Modelling," Economics Papers 2003-W17, Economics Group, Nuffield College, University of Oxford.
    4. Cheong, Chongcheul & Lee, Hyunchul, 2014. "Forecasting with a parsimonious subset VAR model," Economics Letters, Elsevier, vol. 125(2), pages 167-170.
    5. Alvaro Escribano & Genaro Sucarrat, 2011. "Automated model selection in finance: General-to-speci c modelling of the mean and volatility speci cations," Working Papers 2011-09, Instituto Madrileño de Estudios Avanzados (IMDEA) Ciencias Sociales.
    6. Lein, Sarah M. & León-Ledesma, Miguel A. & Nerlich, Carolin, 2008. "How is real convergence driving nominal convergence in the new EU Member States?," Journal of International Money and Finance, Elsevier, vol. 27(2), pages 227-248, March.
    7. Genaro Sucarrat & Alvaro Escribano, 2012. "Automated Model Selection in Finance: General-to-Specific Modelling of the Mean and Volatility Specifications," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(5), pages 716-735, October.
    8. Carl Bonham & Calla Wiemer, 2013. "Chinese saving dynamics: the impact of GDP growth and the dependent share," Oxford Economic Papers, Oxford University Press, vol. 65(1), pages 173-196, January.
    9. Pu Chen & Chih-Ying Hsiao, 2010. "Causal Inference for Structural Equations: With an Application to Wage-Price Spiral," Computational Economics, Springer;Society for Computational Economics, vol. 36(1), pages 17-36, June.
    10. Bonham, Carl & Gangnes, Byron & Zhou, Ting, 2009. "Modeling tourism: A fully identified VECM approach," International Journal of Forecasting, Elsevier, vol. 25(3), pages 531-549, July.
    11. Abhijit Sharma & Kelvin G Balcombe & Iain M Fraser, 2009. "Non-renewable resource prices: Structural breaks and long term trends," Economics Bulletin, AccessEcon, vol. 29(2), pages 805-819.
    12. Allison Zhou & Carl Bonham & Byron Gangnes, 2007. "Modeling the supply and demand for tourism: a fully identified VECM approach," Working Papers 200717, University of Hawaii at Manoa, Department of Economics.
    13. Sucarrat, Genaro & Escribano, Álvaro, 2009. "Automated financial multi-path GETS modelling," UC3M Working papers. Economics we093620, Universidad Carlos III de Madrid. Departamento de Economía.
    14. Julia Campos & Neil R. Ericsson & David F. Hendry, 2005. "General-to-specific modeling: an overview and selected bibliography," International Finance Discussion Papers 838, Board of Governors of the Federal Reserve System (U.S.).

    More about this item


    Model selection; Impulse responses; Vector autoregression; Structural VAR; Causal order; Data mining;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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