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More powerful cointegration tests with non-normal errors

More powerful cointegration tests with non-normal errors Abstract In this paper, we suggest new cointegration tests that can become more powerful in the presence of non-normal errors. Non-normal errors will not pose a problem in usual cointegration tests even when they are ignored. However, we show that they can become useful sources to improve the power of the tests when we use the “residual augmented least squares” (RALS) procedure to make use of nonlinear moment conditions driven by non-normal errors. The suggested testing procedure is easy to implement and it does not require any non-linear estimation techniques. We can exploit the information on the non-normal error distribution that is already available but ignored in the usual cointegration tests. Our simulation results show significant power gains over existing cointegration tests in the presence of non-normal errors. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Studies in Nonlinear Dynamics & Econometrics de Gruyter

More powerful cointegration tests with non-normal errors

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Publisher
de Gruyter
Copyright
Copyright © 2015 by the
ISSN
1081-1826
eISSN
1558-3708
DOI
10.1515/snde-2013-0060
Publisher site
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Abstract

Abstract In this paper, we suggest new cointegration tests that can become more powerful in the presence of non-normal errors. Non-normal errors will not pose a problem in usual cointegration tests even when they are ignored. However, we show that they can become useful sources to improve the power of the tests when we use the “residual augmented least squares” (RALS) procedure to make use of nonlinear moment conditions driven by non-normal errors. The suggested testing procedure is easy to implement and it does not require any non-linear estimation techniques. We can exploit the information on the non-normal error distribution that is already available but ignored in the usual cointegration tests. Our simulation results show significant power gains over existing cointegration tests in the presence of non-normal errors.

Journal

Studies in Nonlinear Dynamics & Econometrics – de Gruyter

Published: Sep 1, 2015

References

  • IV Threshold Cointegration Tests and the Rule Modeling
    Enders
  • Exploring Equilibrium Relationship in Econometrics through Static Models : Some Monte Carlo Evidence of and
    Banerjee
  • IV Threshold Cointegration Tests and the Rule Modeling
    Enders
  • Testing for an Unstable Root in Conditional and Structural Error Correction Models of
    Boswijk
  • Dynamic Specification Cointegration of and
    Boswijk
  • tests for Threshold Cointegration
    Li
  • Error - Correction Mechanism Tests for Cointegration in a Single - Equation Framework
    Banerjee
  • Ericsson Distributions of Error Correction Tests for Cointegration
    MacKinnon
  • Rethinking the Univariate Approach to Unit Root Testing
    Hansen
  • Im More Powerful Unit Root Tests with Non - normal Errors Festschrift in Honor of New York
    Lee
  • The Power of Single Equation Tests for Cointegration when the Cointegrating Vector is Prespecified
    Zivot
  • Ericsson The of Cointegration Tests of and
    Kremers
  • Error - Correction Mechanism Tests for Cointegration in a Single - Equation Framework
    Banerjee
  • Im More Efficient Estimation under Non - normality When Higher Moments Do Not Depend on the Regressors Using Residual - augmented Least Squares of
    Schmidt
  • Asymptotic Inference on Cointegrating Rank in Partial Systems of
    Harbo
  • Im More Powerful Unit Root Tests with Non - normal Errors Festschrift in Honor of New York
    Lee
  • The Power of Single Equation Tests for Cointegration when the Cointegrating Vector is Prespecified
    Zivot
  • Ericsson The of Cointegration Tests of and
    Kremers
  • Rethinking the Univariate Approach to Unit Root Testing
    Hansen
  • More Powerful Cointegration Tests of Statistical Computation and Simulationforthcoming
    Lee
  • Ericsson Distributions of Error Correction Tests for Cointegration
    MacKinnon
  • More Powerful Cointegration Tests of Statistical Computation and Simulationforthcoming
    Lee
  • Co Error Correction Representation Estimation Testing
    Engle
  • tests for Threshold Cointegration
    Li
  • Asymptotic Inference on Cointegrating Rank in Partial Systems of
    Harbo
  • Im More Efficient Estimation under Non - normality When Higher Moments Do Not Depend on the Regressors Using Residual - augmented Least Squares of
    Schmidt
  • Testing for an Unstable Root in Conditional and Structural Error Correction Models of
    Boswijk
  • Co Error Correction Representation Estimation Testing
    Engle
  • Exploring Equilibrium Relationship in Econometrics through Static Models : Some Monte Carlo Evidence of and
    Banerjee
  • Dynamic Specification Cointegration of and
    Boswijk