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Comparison of Model Reduction Methods for VAR Processes

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Author Info

  • Ralf Brüggemann

    () (Humboldt-Universität zu Berlin, Germany)

  • Hans-Martin Krolzig

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

  • Helmut Lütkepohl

    () (Humboldt-Universität zu Berlin and European University Institute, Italy)

Abstract

The objective of this study is to compare alternative computerized model-selection strategies in the context of the vector autoregressive (VAR) modeling framework. The focus is on a comparison of subset modeling strategies with the general-to-specific reduction approach automated by PcGets. Different measures of the possible gains of model selection are considered: (i) the chances of finding the `correct' model, that is, a model which contains all necessary right-hand side variables and is as parsimonious as possible, (ii) the accuracy of the implied impulse-responses and (iii) the forecast performance of the models obtained with different specification algorithms. In the Monte Carlo experiments, the procedures recover the DGP specification from a large VAR with anticipated size and power close to commencing from the DGP itself when evaluated at the empirical size. We find that subset strategies and PcGets are close competitors in many respects, with the forecast comparison indicating a clear advantage of the PcGets algorithm.

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File URL: http://www.nuff.ox.ac.uk/economics/papers/2003/W13/bkl02.pdf
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Bibliographic Info

Paper provided by Economics Group, Nuffield College, University of Oxford in its series Economics Papers with number 2003-W13.

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Length: 20 pages
Date of creation: 02 Apr 2003
Date of revision:
Handle: RePEc:nuf:econwp:0313

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Web page: http://www.nuff.ox.ac.uk/economics/

Related research

Keywords: Model selection; Vector autoregression; Subset model; Lag order determination; Data mining;

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References

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  1. David Hendry & Hans-Martin Krolzig, 2000. "Computer Automation of General-to-Specific Model Selection Procedures," Economics Series Working Papers 3, University of Oxford, Department of Economics.
  2. Hans-Martin Krolzig, 2001. "General--to--Specific Reductions of Vector Autoregressive Processes," Computing in Economics and Finance 2001 164, Society for Computational Economics.
  3. repec:oxf:wpaper:003 is not listed on IDEAS
  4. Hans-Martin Krolzig, 2000. "Computer Automation of General-to-Specific Model Selection Procedures," Econometric Society World Congress 2000 Contributed Papers 0411, Econometric Society.
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Cited by:
  1. Cubadda, Gianluca & Hecq, Alain & Palm, Franz C., 2009. "Studying co-movements in large multivariate data prior to multivariate modelling," Open Access publications from Maastricht University urn:nbn:nl:ui:27-22876, Maastricht University.
  2. 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.
  3. Hans-Martin Krolzig, 2003. "General-to-Specific Model Selection Procedures for Structural Vector Autoregressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 769-801, December.
  4. Jana Eklund & George Kapetanios, 2008. "A Review of Forecasting Techniques for Large Data Sets," Working Papers 625, Queen Mary, University of London, School of Economics and Finance.
  5. Alejandro Gaytán González & Jesús R. González García, 2006. "Structural Changes in the Transmission Mechanism of Monetary Policy in Mexico: A Non-linear VAR Approach," Working Papers 2006-06, Banco de México.
  6. Kapetanios, George, 2007. "Variable selection in regression models using nonstandard optimisation of information criteria," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 4-15, September.
  7. Dietmar Maringer & Peter Winker, 2004. "Optimal Lag Structure Selection in VEC-Models," Computing in Economics and Finance 2004 155, Society for Computational Economics.

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