Nowcasting Industrial Production Using Uncoventional Data Sources

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: ETLA Working Papers ; No. 80

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models ; Large Data Sets: Modeling and Analysis ; Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
• Subject: Flash Estimates ; Machine Learning ; Big Data ; Nowcasting

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Fornaro, Paolo
• Event: Veröffentlichung
• (who): The Research Institute of the Finnish Economy (ETLA)
• (where): Helsinki
• (when): 2020

Further information

• Handle: http://hdl.handle.net/10419/237365
• Last update: 10.03.2025, 11:42 AM CET