Computational Economics

, Volume 16, Issue 1–2, pp 87–103 | Cite as

Optimized Multivariate Lag Structure Selection

  • Peter Winker


Model selection – choosing the relevant variables and structures –is a central task in econometrics. Given a limited number of observations,estimation and inference depend on this choice. A frequently treatedmodel-selection problem arises in multivariate autoregressive models, wherethe problem reduces to the choice of a dynamic structure. In most applicationsthis choice is based either on some ad hoc procedure or on a search within avery small subset of all possible models. In this paper the selection isperformed using an explicit optimization approach for a given informationcriterion. Since complete enumeration of all possible lag structures isinfeasible even for moderate dimensions, the global optimization heuristic ofthreshold accepting is implemented. A simulation study compares this approachwith the standard `take all up to the kth lag' approach. It is foundthat, if the lag structure of the true model is sparse, the thresholdaccepting optimization approach gives far better approximations.

model selection VAR identification heuristic optimization threshold accepting 


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Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Peter Winker
    • 1
  1. 1.Department of EconomicsUniversity of MannheimMannheimGermany

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