This paper considers the maximum likelihood estimation of a class of stationary and invertible vector autoregressive fractionally integrated moving-average (VARFIMA) processes considered in Luceno (1996). The coverage of this class of VARFIMA processes is quite general and includes the model...

Models for the twelve-month-ahead US rate of inflation, measured by the chain weighted consumer expenditure deflator, are estimated for 1974-99 and subsequent pseudo out-of-sample forecasting performance is examined. Alternative forecasting approaches for different information sets are compared...

In this paper, we develop a parametric test procedure for multiple horizon "Granger" causality and apply the procedure to the well established problem of determining causal patterns in aggregate monthly U.S. money and output. As opposed to most papers in the parametric causality literature, we...

For a multivariate time series model with structural breaks, explicit representations of the Beveridge-Nelson and Granger-Gonzalo-Proietti permanent trends are derived from the Johansen maximum likelihood estimates.

This paper proposes an extension to scalar component methodology for the identification and estimation of VARMA models. The complete methodology determines the exact positions of all free parameters in any VARMA model with a predetermined embedded scalar component structure. This leads to an...

This paper focuses on the diagnostic checking of vector ARMA (VARMA) models with multivariate GARCH errors. For a fitted VARMA-GARCH model with Gaussian or Student-t innovations, we derive the asymptotic distributions of autocorrelation matrices of the cross-product vector of standardized...

In this paper, we argue that there is no compelling reason for restricting the class of multivariate models considered for macroeconomic forecasting to VARs given the recent advances in VARMA modelling methodology and improvements in computing power. To support this claim, we use real...

The vector innovation structural time series framework is proposed as a way of modelling a set of related time series. Like all multi-series approaches, the aim is to exploit potential inter-series dependencies to improve the fit and forecasts. A key feature of the framework is that the series...

Geweke studied the measure of linear dependence and spectral feedback for grouped multivariate time series. This paper applies the measure of linear dependence and spectral feedback to examining the relationship between grouped variables of economy and stock market indices. Putting economic...

In many real phenomena the behaviour of a certain variable, subjected to different regimes, depends on the state of other variables or the same variable observed in other subjects, so the knowledge of the state of the latter could be important to forecast the state of the former. In this paper a...