Confidence intervals for long memory regressions

Confidence Intervals for Long Memory Regressions

Small Sample Bootstrap Confidence Intervals for Long-Memory Parameter

The log periodogram regression is widely used in empirical applications because of its simplicity, since only a least squares regression is required to estimate the memory parameter, d, its good asymptotic properties and its robustness to misspecification of the short term behavior of the series. However, the asymptotic distribution is a poor approximation of the (unknown) finite sample distrib...

Bootstrap confidence intervals in a switching regressions model

Traditional switching regression methods produce slope and intercept estimates conditional on the change point estimate, with confidence intervals that overstate their precision. This paper describes the problem and a bootstrap alternative. Extensive sampling experiments confirm that the traditional methods overstate precision, and that bootstrap confidence intervals are far more accurate.

Improved Nonparametric Confidence Intervals in Time Series Regressions

Confidence intervals in econometric time series regressions suffer from notorious coverage problems. This is especially true when the dependence in the data is noticeable and sample sizes are small to moderate, as is often the case in empirical studies. This paper suggests using the studentized block bootstrap and discusses practical issues, such as the choice of the block size. A particular da...

Comparison of five introduced confidence intervals for the binomial proportion

So far many confidence intervals were introduced for the binomial proportion. In this paper, our purpose is comparing five well known based on their exact confidence coefficient and average coverage probability.