In many cases, multiple time series can be viewed as realizations of the same underlying process and such data usually accumulate in time. The historic time-series data provide important information for our current prediction. In this paper, we extend the traditional state-space model to a...

This paper discusses the asymptotics of two-stage least squares estimator of the parameters of ARCH models. The estimator is easy to obtain since it involves solving two sets of linear equations. At the same time, the estimator has the same asymptotic efficiency as that of the widely used...

The smoothing filter is appropriately modified for state space models with an unknown initial condition. Modifications are confined to an initial stretch of the data. An application illustrates procedures. Copyright 2003 Blackwell Publishing Ltd.

This paper derives the exact distribution of the maximum likelihood estimator of a first-order linear autoregression with an exponential disturbance term. We also show that, even if the process is stationary, the estimator is T-consistent, where T is the sample size. In the unit root case, the...

In this note, we highlight a minor error in the asymptotic distribution of one of the Busetti and Harvey (2001) tests for stationarity in the presence of structural breaks, and provide corrected asymptotic critical values where relevant. In addition, we examine the extent to which finite sample...

We demonstrate that the fast and exact Davies-Harte algorithm is valid for simulating a certain class of stationary Gaussian processes - those with a negative autocovariance sequence for all non-zero lags. The result applies to well known classes of long memory processes: Gaussian fractionally...

Two multistage methods for estimating scalar ARMA models are investigated. Both estimate innovations using an autoregression; these are used to obtain initial ARMA parameter estimates by regression and finally the initial estimates are refined by generalized least squares or nonlinear...

In multivariate time series modelling, we are often led to investigate the existence of a relationship between two time series. Here, we generalize the procedure proposed by Haugh (1976) and extended by El Himdi and Roy (1997) for multivariate stationary ARMA time series to the case of...

In this paper, we propose two test statistics for testing serial correlation in semiparametric time series model that could allow lagged dependent variables as explanatory variables. The first one is testing for zero first-order serial correlation and the second is for testing higher-order...