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Comparing the small sample properties of two break Lagrange Multiplier unit root tests

Author

Listed:
  • Paresh Kumar Narayan

    (School of Accounting, Economics and Finance, Deakin University)

  • Stephan Popp

    (University of Essen-Duisburg)

Abstract

In this note, we examine the size and power properties and the break date estimation accuracy of the Lee and Strazicich (LS, 2003) two break endogenous unit root test, based on two different break date selection methods: minimising the test statistic and minimising the sum of squared residuals (SSR). Our results show that the performance of both Models A and C of the LS test are superior when one uses the minimising SSR procedure.

Suggested Citation

  • Paresh Kumar Narayan & Stephan Popp, 2012. "Comparing the small sample properties of two break Lagrange Multiplier unit root tests," Economics Bulletin, AccessEcon, vol. 32(2), pages 1082-1090.
    • Handle: RePEc:ebl:ecbull:eb-10-00630
    as

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    References listed on IDEAS

    as
    1. Junsoo Lee & Mark C. Strazicich, 2013. "Minimum LM unit root test with one structural break," Economics Bulletin, AccessEcon, vol. 33(4), pages 2483-2492.
    2. 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.
    3. Junsoo Lee & Mark C. Strazicich, 2003. "Minimum Lagrange Multiplier Unit Root Test with Two Structural Breaks," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 1082-1089, November.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Lagrange Multiplier unit root test; structural breaks; break date estimation;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

    Statistics

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