Keep it simple: on specific-to-general predictor selection for time series forecasting in the short, medium and long run

Abstract

This paper describes the forecasting performance of general-to-specific and specific-to-general predictor selection within specifications fitting into the class of (approximate) linear autoregressions. Short, medium and long horizon forecasting exercises are distinguished. Regarding the latter, iterative prediction is compared with direct conditioning on available time series information. Ex ante forecasting results are provided for 495 real macro-economic and financial time series recently collected for 25 economies and the Euro area [A. Inouea and L. Kilian, On the selection of forecasting models, J. Econ. 130 (2006), pp. 273–306]. Almost 9000 single predictions enter the modelling comparison. Overall, specific-to-general predictor selection turns out to offer preferable prediction outcomes in terms of statistical and more economic loss functions. With regard to medium (long) term prediction, the analysis is supportive for direct (iterative) multistep prediction.