2009年2月
COVARIANCE-BASED ORTHOGONALITY TESTS FOR REGRESSORS WITH UNKNOWN PERSISTENCE
ECONOMETRIC THEORY
- ,
- 巻
- 25
- 号
- 1
- 開始ページ
- 63
- 終了ページ
- 116
- 記述言語
- 英語
- 掲載種別
- DOI
- 10.1017/S0266466608090038
- 出版者・発行元
- CAMBRIDGE UNIV PRESS
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, tinder 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 and 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 a rich set of 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. 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.
- リンク情報
- ID情報
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- DOI : 10.1017/S0266466608090038
- ISSN : 0266-4666
- Web of Science ID : WOS:000262804400003