The vector autoregressive (VAR) approach is useful in many situations involving model development for multivariables
time series. VAR model was utilised in this study and applied in modelling and forecasting four meteorological variables.
The variables are n rainfall data, humidity, wind speed and temperature. However, the model failed to address the
heteroscedasticity problem found in the variables, as such, multivariate GARCH, namely, dynamic conditional correlation
(DCC) was incorporated in the VAR model to confiscate the problem of heteroscedasticity. The results showed that the use
of the VAR coupled with the recognition of time-varying variances DCC produced good forecasts over long forecasting
horizons as compared with VAR model alone.