Half-life estimation based on the bias-corrected bootstrap: a highest density region approach

Jae H Kim, Param Silvapulle, Rob J Hyndman
(2007) Computational Statistics and Data Analysis 51(7), 3418-3432

DOI

The half-life is defined as the number of periods required for the impulse response to a unit shock to a time series to dissipate by half. It is widely used as a measure of persistence, especially in international economics to quantify the degree of mean-reversion of the deviation from an international parity condition. Several studies have proposed bias-corrected point and interval estimation methods. However, they have found that the confidence intervals are rather uninformative with their upper bound being either extremely large or infinite. This is largely due to the distribution of the half-life estimator being heavily skewed and multi-modal. In this paper, we propose a bias-corrected bootstrap procedure for the estimation of half-life, adopting the highest density region (HDR) approach to point and interval estimation. Our Monte Carlo simulation reveals that the bias-corrected bootstrap HDR method provides an accurate point estimator, as well as tight confidence intervals with excellent coverage rates. As an application, the proposed method is used for half-life estimation of the real exchange rates of seventeen industrialized countries. The results indicate much faster rates of mean-reversion than those reported in previous studies.

Keywords: Bias-correction, bootstrapping, confidence interval, half-life, highest density region.