Predictive accuracy of time series models applied to economic data: the European countries retail trade

Abstract

Modeling and accurately forecasting trend and seasonal patterns of a time series is a crucial activity in economics. The main propose of this study is to evaluate and compare the performance of three traditional forecasting methods, namely the ARIMA models and their extensions, the classical decomposition time series associated with multiple linear regression models with correlated errors, and the Holt–Winters method. These methodologies are applied to retail time series from seven different European countries that present strong trend and seasonal fluctuations. In general, the results indicate that all the forecasting models somehow follow the seasonal pattern exhibited in the data. Based on mean squared error (MSE), root mean squared error (RMSE), mean absolute percentage error (MAPE), mean absolute scaled error (MASE) and U-Theil statistic, the results demonstrate the superiority of the ARIMA model over the other two forecasting approaches. Holt–Winters method also produces accurate forecasts, so it is considered a viable alternative to ARIMA. The performance of the forecasting methods in terms of coverage rates matches the results for accuracy measures.