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Power Properties of Linearity Tests for Time Series

Author

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  • Teräsvirta, Timo

    (Department of Economic Statistics)

Abstract

This paper examines the power properties of several linearity tests applied in time series analysis. The tests are the ones Lee et al. (1993) used in their Monte Carlo study. The main tool used for power comparisons in this paper is the Pitman asymptotic relative efficiency. The results generally strengthen the outcome of the simulations and complement some results in Lee et al. (1993). They also suggest guidelines for designing Monte Carlo experiments for linearity tests.

Suggested Citation

  • Teräsvirta, Timo, 1996. "Power Properties of Linearity Tests for Time Series," SSE/EFI Working Paper Series in Economics and Finance 94, Stockholm School of Economics.
    • Handle: RePEc:hhs:hastef:0094
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    Cited by:

    1. Castle, Jennifer L. & Hendry, David F., 2010. "A low-dimension portmanteau test for non-linearity," Journal of Econometrics, Elsevier, vol. 158(2), pages 231-245, October.
    2. Lee Tae-Hwy & Xi Zhou & Zhang Ru, 2013. "Testing for Neglected Nonlinearity Using Artificial Neural Networks with Many Randomized Hidden Unit Activations," Journal of Time Series Econometrics, De Gruyter, vol. 5(1), pages 61-68, January.
    3. Q.Farooq Akram & Øyvind Eitrheim & Lucio Sarno, 2006. "Non-linear Dynamics in Output, Real Exchange Rates and Real Money Balances: Norway, 1830-2003," Contributions to Economic Analysis, in: Nonlinear Time Series Analysis of Business Cycles, pages 333-377, Emerald Group Publishing Limited.
    4. Berg, Nathan, 2004. "No-decision classification: an alternative to testing for statistical significance," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 33(5), pages 631-650, November.

    More about this item

    Keywords

    Bilinear model; local asymptotic power; nonlinear time series; Pitman asymptotic relative efficiency; threshold autoregressive model;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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