This paper studies the asymptotic and nite-sample performance ofpenalized regression methods when different selectors of theregularization parameter are used under the assumption that the truemodel is, or is not, included among the candidate model. In the lattersetting, we relax assumptions in...

We propose a direct and convenient reduced-bias estimator of predictive regression coefficients, assuming that the regressors are Gaussian first-order autoregressive with errors that are correlated with the error series of the dependent variable. For the single-regressormodel, Stambaugh (1999)...

We consider the problem of selecting the number of frequencies, m, in a log-periodogram regression estimator of the memory parameter d of a Gaussian long-memory time series. It is known that under certain conditions the optimal m, minimizing the mean squared error of the corresponding estimator...

Studies of predictive regressions analyze the case where yt is predicted by xt-1 with xt being first-order autoregressive, AR(1). Under some conditions, the OLS- estimated predictive coefficient is known to be biased. We analyze a predictive model where yt is predicted by xt-1, xt-2,... xt-p...