In this note, several aspects of a recently proposed specification test in nonparametric models driven by an absolutely regular process are discussed. In particular, we give a more detailed asymptotic analysis of tests based on kernel methods under fixed alternatives using a central limit...

We propose a test for separability of the correlation structure of a multivariate time series. We construct test statistics based on a spectral density matrix estimated in a nonparametric way and derive their asymptotic properties. We use simulation to check the performance in finite samples....

In this paper we consider the time series dependence, stationarity, and higher moments issues of a family of first-order conditionally heteroskedastic in mean models with a possibly time-varying mean parameter. The interest in these models lies in the fact that economic theory and physics often...

In this paper, we study a stationary ARCH(q) model with parameters &agr;_0,&agr;_1,&agr;_2, H ,&agr;_q. It is known that the model requires all parameters &agr;_i to be non-negative, but sometimes the usual algorithm based on Newton-Raphson's method leads us to obtain some negative solutions. So this study proposes...

Peña and Box [Journal of Americal Statistical Association (1987) Vol. 82, PP. 836-843] proposed a factor model which aimed to explore the possibility of using lower-dimensional series to represent or explain an observed higher-dimensional multiple time series. However, there were no statistics...

We consider the estimation of the location of the pole and memory parameter omega_0 and d of a covariance stationary process with spectral density Copyright 2004 Blackwell Publishing Ltd.

An approach to Bayesian model selection in self-exciting threshold autoregressive (SETAR) models is developed within a reversible jump Markov chain Monte Carlo (RJMCMC) framework. Our approach is examined via a simulation study and analysis of the Zurich monthly sunspots series. We find that the...

In this paper, we study the problem of the nonparametric estimation of the function m in a stochastic volatility model h_t = exp(X_t/2 Lambda )ξ_t, X_t = m(X_t - 1) + η_t, where ξ_t is a Gaussian white noise. We show that the model can be written as an autoregression with errors-in-variables....

This paper addresses the problem of Bayesian inference in autoregressive (AR) processes in the case where the correct model order is unknown. Original hierarchical prior models that allow the stationarity of the model to be enforced are proposed. Obtaining the quantities of interest, such as...

The likelihood function of a seasonal model, Y_t = ρY_t - d + e_t as implemented in computer algorithms under the assumption of stationary initial conditions is a function of ρ which is zero at the point ρ = 1. It is a smooth function for ρ in the above seasonal model with a well-defined...