This paper provides a comprehensive Monte Carlo comparison of different finite-sample bias-correction methods for autoregressive processes. We consider classic situations where the process is either stationary or exhibits a unit root. Importantly, the case of mildly explosive behaviour is...

In this paper we investigate bootstrap-based methods for bias-correcting the first-stage parameter estimates used in some recently developed bootstrap implementations of the co-integration rank tests of Johansen (1996). In order to do so we adapt the framework of Kilian (1998) which estimates...

In this paper we propose a novel method to construct confidence intervals in a class of linear inverse problems. First, point estimators are obtained via a spectral cut-off method depending on a regularisation parameter », that determines the bias of the estimator. Next, the proposed confidence...

In this paper we investigate bootstrap-based methods for bias-correcting the first-stage parameter estimates used in some recently developed bootstrap implementations of the co-integration rank tests of Johansen (1996). In order to do so we adapt the framework of Kilian (1998) which estimates...

Due to their status as "the" benchmark yield for the world's largest government bond market and its importance for US monetary policy, the interest in a "good" forecast of the constant maturity yield of the 10-year U.S. Treasury bond ("T-bond yields") is immense. This paper assesses three...

This paper proposes a new method of interval estimation for the long run response (or elasticity) parameter from a general linear dynamic model. We employ the bias- corrected bootstrap, in which small sample biases associated with the parameter estimators are adjusted in two stages of the...

This paper introduces two easy to calculate estimators with desirable properties for theautoregressive parameter in dynamic panel data models. The estimators are (nearly) unbiased andperform satisfactorily even for small samples in either the time-series or cross-section dimension.

Prominent asset pricing models imply a linear, time-invariant relation between the equity premium and its conditional variance. We propose an approach to estimating this relation that overcomes some of the limitations of the existing literature. First, we do not require any functional form...

US and UK stock returns are highly positively correlated over the period 1918-1999. Using VAR-based variance decompositions, we investigate the nature of this comovement. Excess return innovations are decomposed into news about future dividends, real interest rates, and excess returns. We find...

Changing persistence in time series models means that a structural change from nonstationarity to stationarity or vice versa occurs over time. Such a change has important implications for forecasting, as negligence may lead to inaccurate model predictions. This paper derives generally applicable...