Nowcasting Industrial Production Using Uncoventional Data Sources - Paolo Fornaro (The Research Institute of the Finnish Economy (ETLA), 2020)
In this work, we rely on unconventional data sources to nowcast the year-on-year growth rate of Finnish indus-trial production, for different industries. As predictors, we use real-time truck traffic volumes measured automatically in different geographical locations around Finland, as well as electricity consumption data. In ad-dition to standard time-series models, we look into the adoption of machine learning techniques to compute the predictions.We find that the use of non-typical data sources such as the volume of truck traffic is beneficial, in terms of predictive power, giving us substantial gains in nowcasting performance compared to an autoregressive model. Moreover, we find that the adoption of machine learning techniques improves substantially the accuracy of our predictions in comparison to standard linear mod-els. While the average nowcasting errors we obtain are higher compared to the current revision errors of the official statistical institute, our nowcasts provide clear signals of the overall trend of the series and of sudden changes in growth.
Keywords: C33; C55; E37; 330; Flash Estimates; Machine Learning; Big Data; Nowcasting