A wavelet threshold denoising procedure for multimodel predictions: An application to economic time series

Abstract

Noise-affected economic time series, realizations of stochastic processes exhibiting complex and possibly nonlinear dynamics, are dealt with. This is often the case of time series found in economics, which notoriously suffer from problems such as low signal-to-noise ratios, asymmetric cycles and multiregimes patterns. In such a framework, even sophisticated statistical models might generate suboptimal predictions, whose quality can further deteriorate unless time consuming updating or deeper model revision procedures are carried out on a regular basis. However, when the models' outcomes are expected to be disseminated in timeliness manner (as in the case of Central Banks or national statistical offices), their modification might not be a viable solution, due to time constraints. On the other hand, if the application of simpler linear models usually entails relatively easier tuning-up procedures, this would come at the expenses of the quality of the predictions yielded. A mixed, self-tuning forecasting method is therefore proposed. This is an automatic, 2-stage procedure, able to generate predictions by exploiting the denoising capabilities provided by the wavelet theory in conjunction with a compounded forecasting generator. Its out-of-sample performances are evaluated through an empirical study carried out on macroeconomic time series.