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Pure Significance Tests of the Unit Root Hypothesis Against Nonlinear Alternatives

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  • ANDREW P. BLAKE
  • GEORGE KAPETANIOS

Abstract

. This paper describes artificial neural network based pure significance tests for the unit root hypothesis against nonlinear alternatives. The theoretical properties of the tests are discussed and a Monte Carlo investigation of their small sample properties is undertaken.

Suggested Citation

  • Andrew P. Blake & George Kapetanios, 2003. "Pure Significance Tests of the Unit Root Hypothesis Against Nonlinear Alternatives," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(3), pages 253-267, May.
    • Handle: RePEc:bla:jtsera:v:24:y:2003:i:3:p:253-267
    DOI: 10.1111/1467-9892.00306
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    References listed on IDEAS

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    1. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    2. George Kapetanios, 2003. "Threshold models for trended time series," Empirical Economics, Springer, vol. 28(4), pages 687-707, November.
    3. George Kapetanios, 2000. "Testing for a Unit Root against Nonlinear STAR Models," National Institute of Economic and Social Research (NIESR) Discussion Papers 164, National Institute of Economic and Social Research.
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    5. George Kapetanios & Yongcheol Shin, 2000. "Testing for a Linear Unit Root against Nonlinear Threshold Stationarity," Edinburgh School of Economics Discussion Paper Series 60, Edinburgh School of Economics, University of Edinburgh.
    6. Pesaran, M. Hashem & Potter, Simon M., 1997. "A floor and ceiling model of US output," Journal of Economic Dynamics and Control, Elsevier, vol. 21(4-5), pages 661-695, May.
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    9. Lee, Tae-Hwy & White, Halbert & Granger, Clive W. J., 1993. "Testing for neglected nonlinearity in time series models : A comparison of neural network methods and alternative tests," Journal of Econometrics, Elsevier, vol. 56(3), pages 269-290, April.
    10. Timo Teräsvirta & Chien‐Fu Lin & Clive W. J. Granger, 1993. "Power Of The Neural Network Linearity Test," Journal of Time Series Analysis, Wiley Blackwell, vol. 14(2), pages 209-220, March.
    11. Schmidt, Peter & Phillips, C B Peter, 1992. "LM Tests for a Unit Root in the Presence of Deterministic Trends," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 54(3), pages 257-287, August.
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    Cited by:

    1. Georgios Chortareas & George Kapetanios, 2004. "The Yen Real Exchange Rate may be Stationary after all: Evidence from Non‐linear Unit‐root Tests," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(1), pages 113-131, February.
    2. Georgios Chortareas & George Kapetanios, 2004. "The Yen Real Exchange Rate may be Stationary after all: Evidence from Non‐linear Unit‐root Tests," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(1), pages 113-131, February.
    3. Dimitris Christopoulos, 2006. "Does a non-linear mean reverting process characterize real GDP movements?," Empirical Economics, Springer, vol. 31(3), pages 601-611, September.
    4. Lee Jinu, 2019. "A Neural Network Method for Nonlinear Time Series Analysis," Journal of Time Series Econometrics, De Gruyter, vol. 11(1), pages 1-18, January.
    5. Lavan Mahadeva and Paul Robinson, 2004. "Unit Root Testing in a Central Bank," Handbooks, Centre for Central Banking Studies, Bank of England, number 22, April.

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