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Lag-length Selection in VAR-models Using Equal and Unequal Lag-Length Procedures


  • Gredenhoff, Mikael

    (Dept. of Economic Statistics, Stockholm School of Economics)

  • Karlsson, Sune

    (Dept. of Economic Statistics, Stockholm School of Economics)


It is well known that inference in vector autoregressive models depends crucially on the choice of lag-length. Various lag-length selection procedures have been suggested and evaluated in the literature. In these evaluations the possibility that the true model may have unequal lag-length has, however, received little attention. In this paper we investigate how sensitive lag-length estimation procedures, based on assumptions of equal or unequal lag-lengths, are to the true model structure. The procedures used in the paper are based on information criteria and we give results for AIC, HQ and BIC. In the Monte Carlo study we generate data from a variety of VAR-models with properties similar to macro-economic time-series. We find that the commonly used procedure based on equal lag-length together with AIC and HQ performs well in most cases. The procedure (due to Hsiao) allowing for unequal lag-lengths produce reasonable results when the true model has unequal lag-length. The Hsiao procedure also tend to do better in models with a more complicated lag structure.

Suggested Citation

  • Gredenhoff, Mikael & Karlsson, Sune, 1997. "Lag-length Selection in VAR-models Using Equal and Unequal Lag-Length Procedures," SSE/EFI Working Paper Series in Economics and Finance 177, Stockholm School of Economics.
  • Handle: RePEc:hhs:hastef:0177

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    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Janakiramanan, Sundaram & Lamba, Asjeet S., 1998. "An empirical examination of linkages between Pacific-Basin stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 8(2), pages 155-173, June.
    2. Yang, Jack J. W., 2002. "The information spillover between stock returns and institutional investors' trading behavior in Taiwan," International Review of Financial Analysis, Elsevier, vol. 11(4), pages 533-547.
    3. Peter Winker & Dietmar Maringer, 2004. "Optimal Lag Structure Selection in VEC-Models," Contributions to Economic Analysis, in: New Directions in Macromodelling, pages 213-234, Emerald Group Publishing Limited.

    More about this item


    Vector autoregression; Order selection; Information Criteria; Monte Carlo simulation.;
    All these keywords.

    JEL classification:

    • 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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