We consider a model <italic>Y</italic>_null = σ_nullη_null in which (σ_null) is not independent of the noise process (η_null) but σ_null is independent of η_null for each <italic>t</italic>. We assume that (σ_null) is stationary, and we propose an adaptive estimator of the density of ln(σ_null²) based on the observations <italic>Y</italic>_null....

In this paper, we study new definitions of noncausality, set in a continuous time framework, illustrated by the intuitive example of stochastic volatility models. Then, we define CIMA processes (i.e., processes admitting a continuous time invertible moving average representation), for which...

Consistency, asymptotic normality, and efficiency of the maximum likelihood estimator for stationary Gaussian time series were shown to hold in the short memory case by Hannan (1973, <italic>Journal of Applied Probability</italic> 10, 130–145) and in the long memory case by Dahlhaus (1989, <italic>Annals of Statistics</italic>...

This paper develops a generalized autoregressive conditional correlation (GARCC) model when the standardized residuals follow a random coefficient vector autoregressive process. As a multivariate generalization of the Tsay (1987, <italic>Journal of the American Statistical Association</italic> 82, 590–604)...

A typical statistic encountered can be characterized as a ratio of polynomials of arbitrary degree in a random vector. This vector may possess any admissible cumulant structure. We provide in this paper general formulae for the effect of nonnormality on the density and distribution functions of...

It is common for an applied researcher to use filtered data, like seasonally adjusted series, for instance, to estimate the parameters of a dynamic regression model. In this paper, we study the effect of (linear) filters on the distribution of parameters of a dynamic regression model with a...