Forecasting external demand using BVAR models

As the impact of monetary policy decisions manifests itself with a lag, decision-makers also need economic forecasts when they make decisions. In this paper, we present a method that may facilitate the integration of incoming data in the external demand forecast faster than is currently possible. The external demand forecast helps to forecast exports and, through that, developments in GDP. In the current practice, for the imports of Hungary’s key trading partners we use the forecasts of international institutions as a starting point. Data received in the meantime can be included in the forecast using expert judgements. With the method described in this paper, we forecast the imports of Hungary’s key trading partners – and with the help thereof – their external demand, relying on BVAR models and using monthly time series (confidence indices, industrial production, orders). Based on the literature, we use the Kalman filter to eliminate the differences in the publication lags of the individual time series. The missing variable is then forecast using the other variables. The forecasts thus obtained perform better than the best ARMA models, and the model containing global imports and the oil price. With one exception, the forecast of the imports of the individual countries is more accurate when prepared on the whole sample, rather than on the rolling sample. The forecast of external demand is also more accurate if we use the whole sample. The most accurate BVAR model used to forecast external demand provides an unbiased forecast and also yields a better forecast of turning points than the models used for comparison. Compared to the forecasts of international institutions, the BVAR forecast performs better when actual import data from the respective year are already available. Thus, compared to previous practice, the novelty is represented by the BVAR methodology and the monthly time series, which can be integrated into the forecast in a formalised manner. Looking ahead, it may also be worthwhile to forecast GDP components using this method.