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Optimal Estimation Strategies for Bivariate Fractional Cointegration Systems

Author

Listed:
  • Marcel Aloy

    (Aix-Marseille University (Aix-Marseille School of Economics), CNRS & EHESS)

  • Gilles de Truchis

    (Aix-Marseille University (Aix-Marseille School of Economics), CNRS & EHESS)

Abstract
Estimation methods of bivariate fractional cointegration models are numerous. In most cases they have non-equivalent asymptotic and finite sample properties, implying diffculties in determining an optimal estimation strategy. In this paper, we address this issue by means of simulations and provide useful guidance to practitioners. Our Monte Carlo study reveals the superiority of techniques that estimate jointly all parameters of interest, over those operating in two steps. In some cases, it also shows that estimators originally designed for the stationary cointegration, have good finite sample properties in non-stationary regions of the parameter space.

Suggested Citation

  • Marcel Aloy & Gilles de Truchis, 2013. "Optimal Estimation Strategies for Bivariate Fractional Cointegration Systems," AMSE Working Papers 1353, Aix-Marseille School of Economics, France, revised 29 Oct 2013.
    • Handle: RePEc:aim:wpaimx:1353
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Fractional cointegration; Monte Carlo simulation; Whittle estimation; Frequency analysis.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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