@article{Maynard:273598,
      recid = {273598},
      author = {Maynard, Alex and Shimotsu, Katsumi},
      title = {Covariance-based orthogonality tests for regressors with  unknown persistence},
      address = {2007-02},
      number = {2110-2018-4247},
      series = {Working Paper No. 1122},
      pages = {54},
      year = {2007},
      abstract = {This paper develops a new test of orthogonality based on a  zero restriction on the covariance between the dependent  variable and the predictor. The test provides a useful  alternative to regression-based tests when conditioning  variables have roots close or equal to unity. In this case  standard predictive regression tests can suffer from  well-documented size distortion. Moreover, under the  alternative hypothesis, they force the dependent variable  to share the same order of integration as the predictor,  whereas in practice the dependent variable often appears  stationary while the predictor may be near-nonstationary.  By contrast, the new test does not enforce the same orders  of integration and is therefore capable of detecting  alternatives to orthogonality that are excluded by the  standard predictive regression model. Moreover, the test  statistic has a standard normal limit distribution for both  unit root and local-to-unity conditioning variables,  without prior knowledge of the local-to-unity parameter. If  the conditioning variable is stationary, the test remains  conservative and consistent. Thus the new test requires  neither size correction nor unit root pre-test. Simulations  suggest good small sample performance. As an empirical  application, we test for the predictability of stock  returns using two persistent predictors, the dividendprice-  ratio and short-term interest rate.},
      url = {http://ageconsearch.umn.edu/record/273598},
      doi = {https://doi.org/10.22004/ag.econ.273598},
}