Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/189398 
Year of Publication: 
2007
Series/Report no.: 
Queen's Economics Department Working Paper No. 1122
Publisher: 
Queen's University, Department of Economics, Kingston (Ontario)
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 dividend-price-ratio and short-term interest rate.
Subjects: 
orthogonality test
covariance estimation
local-to-unity
unit roots
market efficiency
predictive regression
regression imbalance
JEL: 
C12
C22
Document Type: 
Working Paper

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