A bootstrap bias-correction method is applied to statistical inference in the regression model with autocorrelated errors. It is found that this method substantially reduces small-sample size distortions relative to alternative methods proposed in the literature.

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...

Market microstructure noise is a challenge to high-frequency based estimation of the integrated variance, because the noise accumulates with the sampling frequency. In this paper, we analyze the impact of microstructure noise on the realized range-based variance and propose a bias-correction to...

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.

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...

We propose a solution to the measurement error problem that plagues the estimation of the relation between the expected return of the stock market and its conditional variance due to the latency of these conditional moments. We use intra-period returns to construct a nonparametric proxy for 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.