Testing for stationary or persistent coefficient randomness in predictive regressions
研究了预测回归中系数随机性的检验方法，发现当随机系数平稳时，Nyblom的LM检验并非最优，并构造了更有效的检验。应用于美国股票收益数据，推翻了先前结论。
We consider tests for coefficient randomness in predictive regressions and study how they are influenced by the persistence of random coefficient. We show that when the random coefficient is stationary, or I(0), Nyblom’s (Citation1989) LM test loses its optimality (in terms of power), which is established against the alternative of integrated, or I(1), random coefficient. We demonstrate this by constructing a test that is more powerful than the LM test when the random coefficient is stationary, although the test is dominated in terms of power by the LM test when the random coefficient is integrated. The power comparison is made under the sequence of local alternatives that approaches the null hypothesis at different rates depending on the persistence of the random coefficient and which test is considered. We revisit an earlier empirical research and apply the tests considered in this study to the U.S. stock returns data. The result mostly reverses the earlier finding. We also identify periods when predictability manifests.