Staff Working Paper No. 923
By Nikoleta Anesti, Eleni Kalamara and George Kapetanios
By employing large panels of survey data for the UK economy, we aim at reviewing linear approaches for regularisation and dimension reduction combined with techniques from the machine learning literature, like Random Forests, Support Vector Regressions and Neural Networks for forecasting GDP growth at monthly frequency for horizons from one month up to two years ahead. We compare the predictive content of surveys with text based indicators from newspaper articles and a standard macroeconomic data set and extend the empirical evidence on the contribution of survey data against text indicators and more traditional macroeconomic time series in predicting economic activity. Among the linear models, the Ridge and the Partial Least Squares models report the largest gains consistently for most of the forecasting horizons, and for the non‑linear machine learning models, the SVR performs better at shorter horizons compared to the Neural Networks and Random Forest that seem to be more appropriate for longer‑term forecasting. Text based indicators appear to favour more the use of non‑linear models and the expansion of the information set with macroeconomic time series does not appear to add much more predictive power. The largest forecasting gains are overwhelmingly concentrated at the shorter horizons for the majority of models and datasets which provides further empirical support that non‑linear machine learning models appear to be more useful during the Great Recession.
Forecasting UK GDP growth with large survey panels