Small-sample tests for stock return predictability with possibly non-stationary regressors and GARCH-type effects
提出一种基于模拟的检验方法，在回归变量过程完全自由且存在非正态性和GARCH型效应时，仍能有效检验股票收益的可预测性，并发现1948-2014年间期限利差具有预测能力。
We develop a simulation-based procedure to test for stock return predictability with multiple regressors. The process governing the regressors is left completely free and the test procedure remains valid in small samples even in the presence of non-normalities and GARCH-type effects in the stock returns. The usefulness of the new procedure is demonstrated in a simulation study and by examining the ability of a group of financial variables to predict excess stock returns. We find some evidence of predictability during the period 1948–2014, driven entirely by the term spread. This empirical evidence, however, is much weaker over subsamples.