Milan Time Series Seminars

The goal of the Milan Time Series Seminars (MiTSS) is to bring together researchers with common interests in time series econometrics. The seminar provides a forum for researchers to present their work to a broad academic and professional audience.

Each seminar lasts for 60 minutes, followed by an informal discussion, and will be delivered at

University of Milan (Statale) - Department of Economics, Management and Quantitative Methods (DEMM),
University of Milano-Bicocca - Department of Economics, Management and Statistics (DEMS) and its datalab,
Fondazioni Eni Enrico Mattei (FEEM)
Politecnico di Milano - Department of Management, Economics and Industrial Engineering
Bocconi University - BAFFI Centre on Economics, Finance and Regulation and Department of Economics Ettore Bocconi
Catholic University of Milan - Department of Economics and Finance

Scientific Committee:

Andrea Bastianin (University of Milan-Statale and FEEM)

Chiara Casoli (University of Insubria and FEEM)

Fabrizio Iacone (University of Milan-Statale)

Matteo Manera (University of Milano-Bicocca and FEEM)

Massimiliano Marcellino (Bocconi University)

Riccardo Masolo (Catholic University of Milan)

Andrea Monticini (Catholic University of Milan)

Matteo Pelagatti (University of Milano-Bicocca)

Luca Rossini (University of Milan-Statale and FEEM)

Daniele Siena (Politecnico di Milano)

Upcoming seminars for the Academic Year 2025/26:

17 April 2026 at 12:15

Speaker: Matteo Mogliani (Banque de France)

Title: Density Forecast Transformations

Abstract: The popular choice of using a direct forecasting scheme implies that the individual predictions do not contain information on cross-horizon dependence. However, this dependence is needed if the forecaster has to construct, based on direct density forecasts, predictive objects that are functions of several horizons (e.g. when constructing annual-average growth rates from quarter-on-quarter growth rates). To address this issue we propose to use copulas to combine the individual h-step-ahead predictive distributions into a joint predictive distribution. Our method is particularly appealing to practitioners for whom changing the direct forecasting specification is too costly. In a Monte Carlo study, we demonstrate that our approach leads to a better approximation of the true density than an approach that ignores the potential dependence. We show the superior performance of our method in several empirical examples, where we construct (i) quarterly forecasts using month-on-month direct forecasts, (ii) annual-average forecasts using monthly year-on-year direct forecasts, and (iii) annual-average forecasts using quarter-on-quarter direct forecasts.

Location: Politecnico of Milan (Bovisa) - Via R. Lambruschini 4/B, Italy - Aula BL.27.1.2

12 May 2026 at 16:00

Speaker: Giorgio Primiceri (Northwestern University)

Title: TBA

Abstract: TBA

Location: Bocconi University, Via Roentgen 1, Milan

28 May 2026 at 12:15:

Speaker: Laura Coroneo (University of York)

Title: TBA

Abstract: TBA

Location: University of Milan, Via Conservatorio 7, 20122, Milan

04 June 2026 at: