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Regression-based analysis of cointegration systems

Author

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  • Javier Gómez Biscarri
  • Javier Hualde

Abstract

Two estimation procedures dominate the cointegration literature: Johansen's maximum likelihood inference on vector autoregressive error correction models, and estimation of Phillips' triangular forms. This latter methodology is essentially semiparametric, focusing on estimating long run parameters by means of cointegrating regressions, but it is less used in practice than Johansen's approach, since its implementation requires prior knowledge of features such as the cointegrating rank and an appropriate set of non-cointegrated regressors. In this paper we develop a simple and automatic procedure (based on unit root and regression-based cointegration testing) which provides an estimator of the cointegrating rank and data-based just-identifying conditions for the cointegrating parameters (leading to a Phillips' triangular form). A Monte Carlo experiment and an empirical example are also provided.

Suggested Citation

  • Javier Gómez Biscarri & Javier Hualde, 2014. "Regression-based analysis of cointegration systems," Working Papers 780, Barcelona Graduate School of Economics.
  • Handle: RePEc:bge:wpaper:780
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    References listed on IDEAS

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    Cited by:

    1. Marcel Aloy & Gilles Truchis, 2016. "Optimal Estimation Strategies for Bivariate Fractional Cointegration Systems and the Co-persistence Analysis of Stock Market Realized Volatilities," Computational Economics, Springer;Society for Computational Economics, vol. 48(1), pages 83-104, June.
    2. Gomez-Biscarri, Javier & Hualde, Javier, 2015. "A residual-based ADF test for stationary cointegration in I(2) settings," Journal of Econometrics, Elsevier, vol. 184(2), pages 280-294.

    More about this item

    Keywords

    cointegrating space; Phillips' triangular form; Johansen's methodology; regression-based cointegration testing;

    JEL classification:

    • 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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