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MODELSEL: GAUSS module for Model Selection in Time Series Analysis

Author

Listed:
  • Scott Hacker

    (Jonkoping University, Sweden)

  • Abdulnasser Hatemi-J

    (UAE University)

Programming Language

GAUSS

Abstract

In applied research, model selection is an important issue since the true model is not known a priory. Thus, which model should be used is a purely empirical issue. The current module implements the Hacker and Hatemi-J (2022) method for selecting the best time series model among various potential ones using some common information criteria. In addition, model weights based upon these information criteria are provided to consider the weight of evidence supporting one model or set of models over others. These weights could possibly be used for averaging the estimated parameters across models in order to account for model uncertainty. For technical details see Hacker and Hatemi-J (2022) "Model Selection in Time Series Analysis: Using Information Criteria as an Alternative to Hypothesis Testing", Journal of Economic Studies, Forthcoming.

Suggested Citation

  • Scott Hacker & Abdulnasser Hatemi-J, 2021. "MODELSEL: GAUSS module for Model Selection in Time Series Analysis," Statistical Software Components G00019, Boston College Department of Economics.
    • Handle: RePEc:boc:bocode:g00019
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    File URL: http://fmwww.bc.edu/repec/bocode/a/all_models_paper.txt
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    File URL: http://fmwww.bc.edu/repec/bocode/m/msita.prg
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