Consider a vector autoregressive (VAR) model of order d: xt = A1xt−1 + . . .+Adxt−d + t, t = 0,±1,±2, . . . , (1) where xt ∈ R is a random vector, Ai ∈ Rp×p, i = 1, . . . , d are fixed coefficient matrices and t is a vector of zero-mean white noise, i.e., E( t) = 0, E( t t ) = Σ and E( t T t+h) = 0, for h 6= 0. We assume that the noise covariance matrix Σ is positive definite with bounded large...

The Vector Autoregressive (VAR) model, the Error Correction Model (ECM), and the Kalman Filter Model (KFM) are used to forecast UK stock prices. The forecasting performance of the three models is compared using out of sample forecasting. The results show that the forecasting performance of the ECM is better than that of the VAR and the KFM, and that the VAR performs a forecasting better than th...

We propose simulation-based forecasting methods for the noncausal vector autoregressive model proposed by Lanne and Saikkonen (2012). Simulation or numerical methods are required because the prediction problem is generally nonlinear and, therefore, its analytical solution is not available. It turns out that different special cases of the model call for different simulation procedures. Simulatio...

A set of rigorous diagnostic techniques is used to evaluate the forecasting performance of five multivariate time-series models for the U.S. cattle sector. The root-meansquared-error criterion along with an evaluation of the rankings of forecast errors reveals that the Bayesian vector autoregression (BVAR) and the unrestricted VAR (UVAR) models generate forecasts which are superior to both a re...

In multivariate time series, the estimation of the covariance matrix of the observation innovations plays an important role in forecasting as it enables the computation of the standardized forecast error vectors as well as it enables the computation of confidence bounds of the forecasts. We develop an on-line, non-iterative Bayesian algorithm for estimation and forecasting. It is empirically fo...

Direct measures of expectations, derived from survey data, are used in a Vector Autoregressive (VAR) model of actual and expected output series in eight industrial sectors comprising UK Manufacturing. Through the application of the Beveridge-Nelson decomposition, the VAR model is used to measure trend output in the Manufacturing Sector. This measure is compared with alternative trend measures o...

Estimation of cointegrated systems via autoregressive approximation is considered in the framework developed by Saikkonen (1992). The asymptotic properties of the estimated coeecients of the autoregressive ECM (error correction model) and the pure VAR (vector autoregressive) representations are derived under the assumption that the autoregressive order goes to innnity with the sample size. Thes...

This paper applies the hybrid dynamic general-equilibrium, vector autoregressive (DGE-VAR) model developed by Ireland (1999) to Canadian time series. It presents the first Canadian evidence that a hybrid DGE-VAR model may have better out-of-sample forecasting accuracy than a simple, structure-free VAR model. The evidence suggests that estimated DGE models have the potential to add good forecast...

Recent time series methods are applied to the problem of forecasting New Zealand’s real GDP. Model selection is conducted within autoregressive (AR) and vector autoregressive (VAR) classes, allowing for evolution in the form of the models over time. The selections are performed using the Schwarz (1978) BIC and the Phillips-Ploberger (1996) PIC criteria. The forecasts generated by the data-deter...