Event Description

Workshop on Machine Learning in Economic Forecasting and Nowcasting

We are pleased to announce a workshop on Machine Learning in Economic Forecasting and Nowcasting, which will take place on 25 April 2025. The event is hosted by the Department of Finance at Copenhagen Business School.

The workshop is organised by Andrii Babii (UNC-Chapel Hill), Eric Ghysels (UNC-Chapel Hill), Massimiliano Marcellino (Bocconi), and Jonas Striaukas (Copenhagen Business School).

Program:
09.00-09.10     Coffee and light refreshments

09.10-09.30     Welcome and Opening Remarks

Forecasting risk and survival
09.30-10.00     Machine-learning growth at risk by Tobias Adrian, Hongqi Chen, Max-Sebastian Dovì and Ji Hyung Lee

10.00-10.30     High-dimensional censored MIDAS logistic regression for corporate survival forecasting by Wei Miao, Jad Beyhum, Jonas Striaukas and Ingrid Van Keilegom

10.30-10.45     Break

Econometric advancedments in forecasting
10.45-11.15     Sparsity tests for high-dimensional linear regression models in time series by Valentina Corradi, Jack Fosten and Daniel Gutknecht

11.15-11.45     Real-time monitoring and detection of financial bubbles in multiple assets by Taiga Ono and Yoshimasa Uematsu

11.45-12.15     L2-relaxation for Economic Prediction by Zhentao Shi and Yishu Wang

12.15-13.30     Lunch

Inflation forecasting
13.30-14.00     Reddit’s pulse on US inflation: forecasting with large language models by Andrea Del Monaco, Luigi Longo, Juri Marcucci, and Irene Tafani

14.00-14.30     The perils of inflation forecast targeting by Christian Conrad, Zeno Enders and Gernot J. Müller

14.30-14.45     Break

Functional nowcasting with mixed frequency data
14.45-15.15     Nowcasting with functional approaches and mixed-frequency data by Catherine Doz, Laurent Ferrara and Anna Simoni

15.15-15.45     Nowcasting distributions: a functional MIDAS model by Massimiliano Marcellino, Andrea Renzetti and Tommaso Tornese

15.45-16.00     Break

Panel and multivariate nowcasting with mixed frequency data
16.00-16.30     An observation-driven mixed-frequency VAR model by Maurizio Daniele, Heiner Mikosch and Stefan Neuwirth

16.30-17.00     Panel machine learning: nowcasting state-level fiscal variables with mixed-frequency data by Philippe Goulet Coulombe, Massimiliano Marcellino and Dalibor Stevanovic

18.30-21.30     Dinner by invitation