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 differents 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.
|Date of creation:||09 Apr 1996|
|Date of revision:|
|Note:||Type of Document - Word for Windows 2.0; prepared on IBM PC ; to print on HP Laser Jet; pages: 21 ; figures: one figure and one table|
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