Mirko Armillotta

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.

Download full CV here

Email: mirko.armillotta@uniroma2.it

Research

Published Articles

Armillotta M., Gorgi P. (2024) Pseudo-variance quasi-maximum likelihood estimation of semi-parametric time series models. Journal of Econometrics , 246: 105894.
Armillotta M., Tsagris M., and Fokianos K. (2024) Inference for Network Count Time Series with the R Package PNAR. The R Journal, 15(4): 255-269.
Armillotta M., Fokianos K., and Guizzardi A. (2024) Unveiling Venice's hotels competition networks from dynamic pricing digital market. Journal of the Royal Statistical Society Series A: Statistics in Society, 187(1): 130-157.
Armillotta M., Fokianos K. (2023) Nonlinear Network Autoregression. The Annals of Statistics, 51(6): 2526-2552. [Open access version]
Armillotta M., Fokianos K. (2023) Count Network Autoregression. Journal of Time Series Analysis, 45(4): 584-612.
Armillotta M., Luati A. and Lupparelli M. (2022) Observation-driven models for discrete-valued time series. Electronic Journal of Statistics 16(1): 1393-1433.

Other Pubblications

Armillotta M. (2023) Two-stage weighted least squares estimator of multivariate conditional mean observation-driven time series models. Book of short Papers SIS 2023, Pearson, pp. 770-775.
Armillotta M., Luati A. and Lupparelli M. (2023) An overview of ARMA-like models for count and binary data. Trends and Challenges in Categorical Data Analysis, Springer, 2023, pp. 233-274.
Armillotta M., Fokianos K., and Krikidis I. (2023) Bootstrapping Network Autoregressive Models for Testing Linearity. Data Science in Applications, Springer, 2023, pp. 99-116.
Armillotta M., Fokianos K. and Krikidis I. (2022) Generalized Linear Models Network Autoregression. Network Science, Springer, 2022, pp. 112-125.
Armillotta M., Luati A. and Lupparelli M. (2020) Observation-driven models for storm counts. Book of short Papers SIS 2020, Pearson, 2020, pp. 863-868.
Armillotta M., Luati A. and Lupparelli M. (2019) Stationarity of a general class of observation driven models for discrete valued processes. Book of short Papers SIS 2019, Pearson, 2019, pp. 31-39.

Working Papers

Armillotta M., Gorgi P. and Lucas A. (2025) Copula tensor count autoregressions for modeling multidimensional integer-valued time series, submitted.
Armillotta M. (2023) Two-stage weighted least squares estimator of multivariate discrete-valued observation-driven models, under review.

Software

Tsagris M., Armillotta M. and Fokianos K. R Package PNAR: Poisson Network Autoregressive Models.

Curriculum Vitae

Download full CV here

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

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:

Armillotta M., Gorgi P. and Lucas A. (2025) Copula tensor count autoregressions for modeling multidimensional integer-valued time series, submitted.
Armillotta M., Gorgi P. (2024) Pseudo-variance quasi-maximum likelihood estimation of semi-parametric time series models. Journal of Econometrics , 246: 105894.
Armillotta M., Tsagris M., and Fokianos K. (2024) Inference for Network Count Time Series with the R Package PNAR. The R Journal, 15(4): 255-269.
Armillotta M., Fokianos K., and Guizzardi A. (2024) Unveiling Venice's hotels competition networks from dynamic pricing digital market. Journal of the Royal Statistical Society Series A: Statistics in Society, 187(1): 130-157.
Armillotta M. (2023) Two-stage weighted least squares estimator of multivariate discrete-valued observation-driven models, under review.