We analyse the case where a unit-root test is based on a Dickey-Fuller regression the only deterministic term of which is a fixed intercept. Suppose, however, as could well be the case, that the actual data-generating process includes a broken linear trend. It is shown theoretically, and...

Maximum quasi-likelihood estimation is investigated for the NEAR(2) model, an autoregressive time series model with marginal exponential distributions. In certain regions of the parameter space, simulations indicate that maximum quasi-likelihood estimators perform better than two-stage...

This paper derives the autocorrelation function of the squared values of long-memory GARCH processes. Such processes are of much interest as they can produce the long-memory conditional heteroskedasticity that many high-frequency financial time series exhibit. An empirical application...

This paper is concerned with filtering for various types of time series models including the class of generalized ARCH models and stochastic volatility models. We extend the results of Thavaneswaran and Abraham (1988) for some time series models using martingale estimating functions. Nonlinear...

The paper presents an algorithm for computing the asymptotic Fisher information matrix of a possibly seasonal single-input single-output (SISO) time-series model. That matrix is a block matrix whose elements are basically integrals of rational functions over the oriented unit circle. The...

We establish the validity of subsampling confidence intervals for the mean of a dependent series with heavy-tailed marginal distributions. Using point process theory, we focus on GARCH-like time series models. We propose a data-dependent method for the optimal block size selection and...

Standard approximate 1 - &agr; prediction intervals (PIs) need to be adjusted to take account of the error in estimating the parameters. This adjustment may be aimed at setting the (unconditional) probability that the PI includes the value being predicted equal to 1 - &agr;. Alternatively, this...

A transformed metric entropy measure of dependence is studied which satisfies many desirable properties, including being a proper measure of distance. It is capable of good performance in identifying dependence even in possibly nonlinear time series, and is applicable for both continuous and...

The block bootstrap for time series consists in randomly resampling blocks of the original data with replacement and aligning these blocks into a bootstrap sample. Recently several matching schemes for the block bootstraps have been suggested to improve its performance by reduction of bias...