The development of asset pricing models that rely on instrumental variables together with the increased availability of easily accessible economic time-series have renewed interest in predicting security returns. Evaluating the significance of these new research findings, however, is no easy task. Because these asset pricing theory tests are not independent, classical methods of assessing goodness-of-fit are inappropriate. This study investigates the distribution of the maximal R-square when k of m regressors are used to predict security returns. The authors provide a simple procedure that adjusts critical R-square values to account for selecting variables by searching among potential regressors.

We provide a formal test of the liquidity preference hypothesis (LPH), that is, the monotonicity of ex ante term premiums, using nonparametric estimates that do not require a structural model for conditional expected returns. Although the point estimates of the term premiums are consistent with previous conclusions in the literature regarding violations of the LPH, the test statistics are generally insignificant, even when powerful conditioning information is used. These results illustrate the importance of correctly accounting for correlations across maturities and of formally testing the inequality restrictions implied by the LPH.

The development of asset pricing models that rely on instrumental variables together with the increased availability of easily‐accessible economic time‐series have renewed interest in predicting security returns. Evaluating the significance of these new research findings, however, is no easy task. Because these asset pricing theory tests are not independent, classical methods of assessing goodness‐of‐fit are inappropriate. This study investigates the distribution of the maximal when k of m regressors are used to predict security returns. We provide a simple procedure that adjusts critical values to account for selecting variables by searching among potential regressors.

The development of asset pricing models that rely on instrumental variables together with the increased availability of easily-accessible economic time-series have renewed interest in predicting security returns. Evaluating the significance of these new research findings, however, is no easy task. Because these asset pricing theory tests are not independent, classical methods of assessing goodness-of-fit are inappropriate. This study investigates the distribution of the maximal R2 when k of m regressors are used to predict security returns. We provide a simple procedure that adjusts critical R2 values to account for selecting variables by searching among potential regressors.

We provide a formal test of the liquidity preference hypothesis (LPH), that is, the monotonicity of ex ante term premiums, using nonparametric estimates that do not require a structural model for conditional expected returns. Although the point estimates of the term premiums are consistent with previous conclusions in the literature regarding violations of the LPH, the test statistics are generally insignificant, even when powerful conditioning information is used. These results illustrate the importance of correctly accounting for correlations across maturities and of formally testing the inequality restrictions implied by the LPH.