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Finite Sample Behaviour of the Level Shift Model using Quasi-Differenced Data

Author

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  • Gabriel Rodriguez

    () (Department of Economics, University of Ottawa)

Abstract

When using quasi-differenced data in a model where a break in the intercept is allowed, asymptotic distributions of the M, ADF, and PT statistics are the same as those in the model where only an intercept and a time trend are included. However, the finite sample behaviour for common sample sizes used in empirical applications, is very different. I calculate finite-sample critical values using two different methods to select the break point and the lag length. Comparison with asymptotic and finite-sample distributions of a model where only a constant and a time trend are included in the set of deterministic components show strong differences. In particular these differences are more clear for the M and PT statistics, while the ADF statistics is not affected too much. An empirical application is performed to show the pitfalls of using asymptotic or finite-sample critical values for a model where a break in the intercept has not been taken into account.

Suggested Citation

  • Gabriel Rodriguez, 2006. "Finite Sample Behaviour of the Level Shift Model using Quasi-Differenced Data," Working Papers 0604E, University of Ottawa, Department of Economics.
  • Handle: RePEc:ott:wpaper:0604e
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    File URL: http://sciencessociales.uottawa.ca/economics/sites/socialsciences.uottawa.ca.economics/files/0604E.pdf
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    References listed on IDEAS

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    Cited by:

    1. Sebastian Fossati, 2013. "Unit root testing with stationary covariates and a structural break in the trend function," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(3), pages 368-384, May.
    2. Apergis, Nicholas & Bowden, Nicholas & Payne, James E., 2015. "Downstream integration of natural gas prices across U.S. states: Evidence from deregulation regime shifts," Energy Economics, Elsevier, vol. 49(C), pages 82-92.

    More about this item

    Keywords

    Unit root test; GLS detrending; structural change; truncation lag; information criteria;

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

    Statistics

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