The purpose of this study is to examine the forecasting abilities of the same multivariate autoregressive model estimated using two methods. The first method is the "exact method" used by the SCA System from Scientific Computing Associates.
All of these methods have been shown to provide forecasts that are more accurate than many econometric methods, which require more resources to implement.
A look at whether the United States’ decision to cease intervention after March 1981 had a perceptible influence on the day-to-day behavior of exchange rates, using the stable paretian distribution.
A comparison of the forecasting abilities of univariate ARIMA, multivariate ARIMA, and VAR, and examination of whether series should be differenced before estimating models for forecasting purposes.
In this paper, we present an application of multivariate time series forecasting in which the data consist of a mixture of quarterly and monthly series.
The Federal Reserve announces targets for the monetary aggregates that are implicitly conditioned on an assumption about future velocity for each of the monetary aggregates.
In this paper, we present a forecasting technique that uses contemporaneous correlations for forecasting in a time series model when only a subset of the variables are available for the current period.
This paper proposes an extension of Granger causality when more than two variables are used in a multivariate time series model, and it is necessary to consider more than one-period-ahead forecasts.
Critics of staggered-reserve accounting have used simple models to show that a disturbance to deposits with no change in total reserves sets in motion an undamped cycle.