Testing procedures for predictive regressions with lagged autoregressive variables imply a suboptimal inference in presence of small violations of ideal assumptions. We propose a novel testing framework resistant to such violations, which is consistent with nearly integrated regressors and...

Testing procedures for predictive regressions with lagged autoregressive variables imply a suboptimal inference in presence of small violations of ideal assumptions. We propose a novel testing framework resistant to such violations, which is consistent with nearly integrated regressors and...

The limit distribution of conventional test statistics for predictability may depend on the degree of persistence of the predictors. Therefore, diverging results and conclusions may arise because of the different asymptotic theories adopted. Using differencing transformations, we introduce a new...

Using a latent variables approach, we estimate the dynamics of dividends and returns in a tractable present-value model with time-varying risks. Expected returns imply a similar return predictability as under homoskedasticity, while expected dividend growth is more persistent and explains a...

We study an equilibrium asset pricing model with several Lucas (1978) trees subject to persistent distress events, where the agent has incomplete information about the state of an underlying common factor and learns from the events occurring to each tree. Contrary to similar asset pricing models...

We propose a multivariate nonparametric technique for generating reliable short-term historical yield curve scenarios and confidence intervals. The approach is based on a Functional Gradient Descent (FGD) estimation of the conditional mean vector and covariance matrix of a multivariate interest...