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A Combinatorial Approach to Piecewise Linear Time Series Analysis


  • Medeiros, Marcelo

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

  • Veiga, Alvaro

    () (Dept. of Electrical Engineering)

  • Resende, Mauricio

    () (Information Sciences Research Center, Algorithms and Optimization Research Department)


Over recent years, several nonlinear time series models have been proposed in the literature. One model that has found a large number of successful applications is the threshold autoregressive model (TAR). The TAR model is a piecewise linear process whose central idea is to change the parameters of a linear autoregressive model according to the value of an observable variable, called the threshold variable. If this variable is a lagged value of the time series, the model is called a self-exciting threshold autoregressive (SETAR) model. In this paper, we propose a heuristic to estimate a more general SETAR model, where the thresholds are multivariate. We formulated the task of finding multivariate thresholds as a combinatorial optimization problem. We developed an algorithm based on a Greedy Randomized Adaptive Search Procedure (GRASP) to solve the problem. GRASP is an iterative randomized sampling technique that has been shown to quickly produce good quality solutions for a wide variety of optimization problems. The proposed model performs well on both simulated and real data.

Suggested Citation

  • Medeiros, Marcelo & Veiga, Alvaro & Resende, Mauricio, 2000. "A Combinatorial Approach to Piecewise Linear Time Series Analysis," SSE/EFI Working Paper Series in Economics and Finance 393, Stockholm School of Economics.
  • Handle: RePEc:hhs:hastef:0393

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    References listed on IDEAS

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    Cited by:

    1. Strikholm, Birgit & Teräsvirta, Timo, 2005. "Determining the Number of Regimes in a Threshold Autoregressive Model Using Smooth Transition Autoregressions," SSE/EFI Working Paper Series in Economics and Finance 578, Stockholm School of Economics, revised 11 Feb 2005.
    2. Baragona Roberto & Cucina Domenico, 2013. "Multivariate Self-Exciting Threshold Autoregressive Modeling by Genetic Algorithms," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 233(1), pages 3-21, February.

    More about this item


    nonlinear time series; piecewise linear models; combinatorial optimization; search heuristic; GRASP;

    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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