A construction of p-values for hypothesis tests based on subsampling and the related m out of n bootstrap is introduced. The p-values are based on a modification of the usual subsampling hypothesis tests that involves an appropriate centering of the subsampled or bootstrapped test statistics as...

In a sample X1,...,XN, independently and identically distributed with distribution F, a linear statistic can be defined, where Ti=ø(Xi), and ø(·) is some function. For this statistics, a 'natural' nonparametric variance estimator is the sample variance , the denominator N-1 often being used...

type="main" xml:id="jtsa12053-abs-0001"When time-series data contain a periodic/seasonal component, the usual block bootstrap procedures are not directly applicable. We propose a modification of the block bootstrap – the generalized seasonal block bootstrap (GSBB) – and show its asymptotic...

We consider the problem of estimating the variance of the partial sums of a stationary time series that has either long memory, short memory, negative/intermediate memory, or is the first-difference of such a process. The rate of growth of this variance depends crucially on the type of memory,...

The well-known ARCH/GARCH models with normal errors account only partly for the degree of heavy tails empirically found in the distribution of financial returns series. Instead of resorting to an arbitrary nonnormal distribution for the ARCH/GARCH residuals we propose a different viewpoint via a...

The quest for the ‘best’ heavy-tailed distribution for ARCH/GARCH residuals appears to still be ongoing. In this connection, we propose a new distribution that arises in a natural way as an outcome of an implicit model. The challenging application of prediction of squared returns is...

This paper considers the problem of variance estimation for the sample mean in the context of long memory and negative memory time series dynamics, adopting the fixed-bandwidth approach now popular in the econometrics literature. The distribution theory generalizes the short memory results of...

The problem of estimating nonparametric regression with associated confidence intervals is addressed. It is shown that through appropriate choice of infinite order kernel, it is possible to construct bootstrap confidence intervals which do not require either explicit bias correction or...