I am an Assistant Professor of Statistics (Tenure-track) at the University of Rome Tor Vergata, Department of Economics and Finance. Prior to my current position, I had a Research Associate position at the Vrije Universiteit Amsterdam and a PostDoc position at the University of Cyprus.
I received the Ph.D. in Statistical Sciences from the University of Bologna.
I have been awarded with the Award for the Best Ph.D. thesis in Statistics 2022 by the Italian Statistical Society. I also held a Marie Skłodowska-Curie Individual Fellowship financed by the European Commission.


Research interests: Discrete-valued time series, Econometrics, Forecasting economic variables, Network time series, Observation-driven models, Time Series Analysis.

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Email: mirko.armillotta@uniroma2.it

Research

Published Articles

Other Pubblications

Working Papers

Software

Curriculum Vitae

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Current Position

  • 2024 - present, Assistant Professor of Statistics (Tenure-track), Department of Economics and Finance, University of Rome Tor Vergata.

Past Positions

  • 2022 - 2024, Research Associate, Department of Econometrics and Data Science, Vrije Universiteit Amsterdam.
  • 2023 - 2024, Candidate Fellow, Tinbergen Institute.
  • 2020 - 2022, Postdoctoral Researcher, Department of Mathematics and Statistics, University of Cyprus.
  • Feb 2020 - May 2020, Visiting Researcher, Department of Mathematics and Statistics, Lancaster University.
  • 2017 - 2019, Teaching tutor, Department of Statistics and Department of Economics, University of Bologna.

Education

  • 2017 - 2021, Ph.D. in Statistical Sciences, University of Bologna.   
    Dissertation title: Essays on discrete valued time series models.
  • 2015 - 2017, M.A. (cum laude) in Statistics, Economics and Business, University of Bologna.
  • 2012 - 2015, B.Sc. (cum laude) in Statistical Sciences, University of Bologna.

Teaching

  • 2023 - 2024, Statistics PM. Teacher and Course coordinator, Vrije Universiteit Amsterdam (English).
  • 2022 - 2023, Econometrics II. Teacher, Vrije Universiteit Amsterdam (English).

Awards & Fellowships

  • Marie Skłodowska-Curie Individual Fellowship financed by the European Commission (2023-2024).
  • Award for the Best Ph.D. thesis in Statistics 2022, awarded by the Italian Statistical Society.

IMARS

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This project has received financial support from the EU Horizon Europe programme under the Marie Skłodowska-Curie grant agreement No.101108797.

Principal Investigator: Mirko Armillotta

Supervisors:
André Lucas, Professor of Financial Econometrics at Vrije Universiteit Amsterdam
Paolo Gorgi, Associate Professor of Econometrics at Vrije Universiteit Amsterdam
Siem Jan Koopman, Professor of Econometrics at Vrije Universiteit Amsterdam

Host institution: Vrije Universiteit Amsterdam
Funding: EUR 187,000
Project duration: April 2023 - Novemebr 2024

Abstract: Policymakers and governmental institutions rely every day on data to make important decisions that impact citizens’ quality of life. For instance, the availability of large amounts of time series data observed in many dimensions and levels of granularity requires the development of new techniques to model and draw conclusions from such complex systems. Multivariate time series data structures typically deal with time series and cross-sectional dimensions. Nowadays, much more complex data structures have appeared, requiring mathematical objects defined in higher dimensions and beyond continuous-valued measurements; e.g. time series of counts related to several types of crimes occurring in different cities. The final object is a three-dimensional data set of counts called a discrete tensor. This research proposal introduces new empirical, econometric models to describe and study such discrete tensor data. Although in social sciences many tensor data possess a discrete structure, the statistical theory for discrete tensors is still under development. These specific data cannot be accommodated by existing tensor models which are specifically tailored for continuous variables and are typically described by simple linear patterns. The goal of the project is to fill the existing gap in the literature by developing new models for discrete-valued tensor data that possess flexible cross-sectional and serial dependence structures. To this aim, we combine a multivariate copula framework for count data and non-linear score-driven models with the existing tensor literature; As a result, we obtain a new model class, called Integer-valued Matrix Autoregressive Score model (IMARS). We then successfully apply these new models to relevant empirical problems in key areas of interest to policymakers, like crime data, allowing to understand the distribution of different types of crimes across geographical areas, forecast their incidence and study spillover effects over a space-time grid.

List of papers funded by the project:

Mirko Armillotta