The Forecasting Accuracy of Five Time Series Models: Evidence from the Portuguese Car Market

This paper compares the out-of-sample forecasting accuracy of five classes of time series models for market shares of the six most important Portuguese car market competitors over different horizons. As representative time series models I employ a random walk with drift (Naive), a univariate ARIMA, a near-VAR and a general BVAR. The out-of-sample forecasts are also compared against forecasts generated from structural econometric market share models (SEM). Using four accuracy measures I find the forecasts from the near-VAR and the BVAR models really more accurate. With regard to these models, I could say that the BVAR model is the best for longer forecasts (12-steps ahead), while the n-VAR is superior over the shorter horizon of one to six steps. JEL Classification: C110, C320, M310.