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Smooth Transition Autoregressive (STAR) Models


  • Dietmar Maringer

    (University of Essex)

  • Mark Meyer

    (University of Giessen)


Non-linear modeling approaches, including Smooth Transition Autoregressive (STAR) models, have attracted a great deal of attention over the last two decades. The empirical application of these models, however, is not always a straightforward task. In particular, parameter estimation and identification of redundant parameters have not been addressed satisfactorily in the literature yet: There are no deterministic numerical methods -- let alone closed form solutions -- to solve these problems reliably. In empirical studies, we find that heuristic approaches such as Threshold Accepting or Evolutionary Methods are capable of solving these problems. Applied to STAR models, we were able to identify solutions that outperform benchmarks provided in the literature. This paper presents how to apply heuristics to the parameter estimation and the model selection problems. Based on computational studies, these methods are compared to traditional approaches

Suggested Citation

  • Dietmar Maringer & Mark Meyer, 2006. "Smooth Transition Autoregressive (STAR) Models," Computing in Economics and Finance 2006 456, Society for Computational Economics.
  • Handle: RePEc:sce:scecfa:456

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    More about this item


    Smooth Transition Autoregressive (STAR); Heuristic Optimization; Model Selection; Estimation;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation


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