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A Gaussian IV estimator of cointegrating relations

Author

Listed:
  • Gunnar Bårdsen
  • Niels Haldrup

    (Department of Economics, University of Aarhus, Denmark)

Abstract

In static single equation cointegration regression models the OLS estimator will have a non-standard distribution unless regressors are strictly exogenous. In the literature a number of estimators have been suggested to deal with this problem, especially by the use of semi-nonparametric estimators. Theoretically ideal instruments can be defined to ensure a limiting Gaussian distribution of IV estimators, but unfortunately such instruments are unlikely to be found in real data. In the present paper we suggest an IV estimator where the Hodrick-Prescott filtered trends are used as instruments for the regressors in cointegrating regressions. These instruments are almost ideal and simulations show that the IV estimator using such instruments alleviate the endogeneity problem extremely well in both finite and large samples.

Suggested Citation

  • Gunnar Bårdsen & Niels Haldrup, 2006. "A Gaussian IV estimator of cointegrating relations," Economics Working Papers 2006-03, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:aarhec:2006-03
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    References listed on IDEAS

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    Cited by:

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    2. Torfinn Harding & Frederick Ploeg, 2013. "Official forecasts and management of oil windfalls," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 20(5), pages 827-866, October.

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    More about this item

    Keywords

    Cointegration; Instrumental variables; Mixed Gaussianity.;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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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