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A New Approach to the Threshold Autoregressive Models

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  • Nuri Celik

Abstract

Time series analyzing is very important tool for economic and financial system. However, recent developments show that financial systems are known in a structural change. Therefore, nonlinear time series have been analyzed for past decades because of these changes. In this paper, we consider Threshold Autoregressive (TAR) model. The most popular method for estimating the parameters and threshold value is least square (LS) method. However, LS method is not robust to the outliers and departures from normality. Therefore, we propose a robust version of estimation in order to provide robust results. Â JEL classification numbers: C01, C22, C13.

Suggested Citation

  • Nuri Celik, 2022. "A New Approach to the Threshold Autoregressive Models," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 11(3), pages 1-1.
    • Handle: RePEc:spt:stecon:v:11:y:2022:i:3:f:11_3_1
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    References listed on IDEAS

    as
    1. Hansen Bruce E., 1997. "Inference in TAR Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 2(1), pages 1-16, April.
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    More about this item

    Keywords

    Threshold Autoregressive Model; Iterated Weighted Least Square; Skew Normal; Long Tailed Symmetric Distribution; Robustness.;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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