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Testing for Cointegration in a Double-LSTR Framework

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  • Grote, Claudia
  • Sibbertsen, Philipp

Abstract

This paper investigates the finite-sample properties of the smooth transition-based cointegration test proposed by Kapetanios et al. (2006) when the data generating process under the alternative hypothesis is a globally stationary second order LSTR model. The provided procedure describes an application to long-run equilibrium relations involving real exchange rates with symmetric behaviour. We utilise the properties of the double LSTR transition function that features unit root behaviour within the inner regime and symmetric behaviour in the outer regimes. Hence, under the null hypothesis we imply no cointegration and globally stationary D-LSTR cointegration under the alternative. As a result of the identification problem the limiting distribution derived under the null hypothesis is non-standard. The Double LSTR is capable of producing three-regime TAR nonlinearity when the transition parameter tends to infinity as well as generating exponential-type nonlinearity that closely approximates ESTR nonlinearity. Therefore, we find that the Double LSTR error correction model has power against both of these alternatives.

Suggested Citation

  • Grote, Claudia & Sibbertsen, Philipp, 2013. "Testing for Cointegration in a Double-LSTR Framework," Hannover Economic Papers (HEP) dp-514, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  • Handle: RePEc:han:dpaper:dp-514
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    References listed on IDEAS

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    More about this item

    Keywords

    Cointegration tests; LSTR; Monte carlo simulation; Nonlinear error correction;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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