I am a professor of econometrics. My research is at the intersection of statistics, numerical analysis, operations research, psychology, economics and management.

- Statistics and econometrics
- Numerical analysis
- Operations research
- Mathematical psychology

PhD in Science, major in Mathematics, 2005

Université Paris Dauphine, Paris (France)

PhD in Management and Industrial Engineering, 1999

Politecnico di Milano, Milano (Italy)

DEA MASE, 1999

Université Paris Dauphine/ENSAE, Paris (France)

MSc in Management and Industrial Engineering, 1996

Politecnico di Milano, Milano (Italy)

Organized within PRIN 20177FX2A7 (PI Alessio Moneta) by Fulvio Corsi, Francesco Feri, Rachele Foschi, Alexia Gaudeul, Alessio Moneta, Raffaello Seri.

Organized with Francesco Guala, Caterina Marchionni and Ivan Moscati, together with Lake Como School of Advanced Studies and INEM (International Network for Economic Method).

Organized with Fabrizio Adani, Garabed Antranikian, Diego Bosco, Marcella Bracale, Loredano Pollegioni, Andreas Pyka, Daniela Ubiali, Andrea Vezzulli.

Organized with Francesco Guala, Caterina Marchionni and Ivan Moscati, together with Lake Como School of Advanced Studies and INEM (International Network for Economic Method).

Organized with Francesco Guala and Ivan Moscati, together with Lake Como School of Advanced Studies and ESHET (European Society for the History of Economic Thought).

Organized within PRIN 2010J3LZEN.

Organized with EMPG (European Mathematical Psychology Group).

*Master Analisi e Valutazione delle Politiche Pubbliche*

- Individui, comportamenti ed istituzioni - 20 hrs (A.Y. 2018-19, 2017-18, 2016-17)
- Politiche per la legalità, di contrasto alla corruzione e all’evasione fiscale - 5 hrs (A.Y. 2016-17)

*Central-German Doctoral Program Economics*

- Econometrics of Competitive and Regulated Markets - 20 hrs (A.Y. 2015-16)

*PhD in Methods and Models for Economic Decision*

- Advanced Econometrics for Decision Making - 20 hrs (A.Y. 2020-21, 2019-20, 2018-19, 2017-18, 2016-17, 2015-16)

*PhD in Econometrics and Empirical Economics*

- Survival analysis - 8 hrs (A.Y. 2007-08)
- Extreme value theory - 8 hrs (A.Y. 2007-08, 2006-07)
- Alternative Factorizations of the Likelihood - 8 hrs (A.Y. 2005-06, 2004-05)

- Econometrics of Competitive and Regulated Markets - 80 hrs (A.Y. 2020-21, 2019-20, 2018-19, 2017-18, 2016-17, 2015-16, 2014-15, 2013-14)
- Econometria - 40 hrs (A.Y. 2020-21, 2019-20, 2018-19, 2017-18, 2016-17, 2015-16, 2014-15, 2013-14, 2009-10, 2008-09, 2007-08, 2006-07)
- Econometria dell’Organizzazione Industriale (Econometrics of Industrial Organization) - 40 hrs (A.Y. 2012-13)
- Statistica per l’Impresa (Marketing Statistics) - 20 hrs (A.Y. 2011-12)
- Econometria delle durate (Advanced Econometrics) - 40 hrs (A.Y. 2009-10, 2008-09, 2007-08)
- Econometria dei mercati finanziari A (Financial Econometrics) - 40 hrs (A.Y. 2008-09, 2006-07)
- Extreme value theory - 6 hrs (A.Y. 2006-07)
- Microeconomia applicata/Microeconometria - 36 hrs (A.Y. 2005-06, 2004-05)
- Econometria dei mercati finanziari B (Advanced Financial Econometrics) - assistant (A.Y. 2005–06, 2004–05, 2003–04)

*Dipartimento di Economia e Produzione*

- Econometria - assistant (A.Y. 1997–98)

*Master MEDEA (Management ed Economia dell’Energia e dell’Ambiente)*

- Metodi matematici per la gestione d’impresa - assistant (A.Y. 2004–05, 2003–04, 1997–98, 1996–97)

*#### Approximation of Stochastic Programming Problems

#### Bioeconomy

#### Inference for Simulation Models

#### Psychological Measurement

#### Statistical Testing

#### Uniformity of Points

In Stochastic Programming, Statistics or Econometrics, one often looks for the solution of optimization problems of the following form: \begin{equation} \inf_{\theta\in\Theta} \mathbb{E}_{\mathbb{P}_{}} g(\cdot,\theta)=\inf_{\theta\in\Theta} \int_{\mathbb{R}^{q}}g(y,\theta)\mathbb{P}_{}(dy) \end{equation} where $\Theta$ is a Borel subset of $\mathbb{R}^{p}$ and $\mathbb{P}$ is a probability measure defined on $\mathbf{Y}=\mathbb{R}^{q}$ endowed with its Borel $\sigma-$field $\mathcal{B}(\mathbf{Y})$ (but more general spaces can be considered).

The increasing importance of biological sciences for creating value added in many economic sectors contributed to the rise of the now popular term “bioeconomy,” referring to “the set of economic activities relating to the invention, development, production and use of biological products and processes” (OECD, 2009), which are characterized by the accent on the reduction of environmental pollution and the adoption of sustainable practices.

Still to be written

Measurement theory is “a field of study that examines the attribution of values to traits, characteristics, or constructs. Measurement theory focuses on assessing the true score of an attribute, such that an obtained value has a close correspondence with the actual quantity” (APA Dictionary of Psychology, 2nd ed.

Still to be written

Quantifying uniformity of a configuration of points on a space is a topic that is receiving growing attention in computer science, physics and mathematics. The problem has interesting connections with statistics, where several tests of uniformity have been introduced.

We consider measures of covering and separation that are expressed through maxima and minima of distances between points of an hypersphere. We investigate the behavior of these measures when applied to a sample of independent and uniformly distributed points. In particular, we derive their asymptotic distributions when the number of points diverges. These results can be useful as a benchmark against which deterministic point sets can be evaluated. Whenever possible, we supplement the rigorous derivation of these limiting distributions with some heuristic reasonings based on extreme value theory. As a by-product, we provide a proof for a conjecture on the hole radius associated to a facet of the convex hull of points distributed on the hypersphere.

We consider the estimation of the entropy of a discretely-supported time series through a plug-in estimator. We provide a correction of the bias and we study the asymptotic properties of the estimator. We show that the widely-used correction proposed by Roulston (1999) is incorrect as it does not remove the $O\left(N^{-1}\right)$ part of the bias while ours does. We provide the asymptotic distribution and we show that it differs when the values taken by the marginal distribution of the process are equiprobable (a situation that we call *degeneracy*) and when they are not. We introduce estimators of the bias, the variance and the distribution under degeneracy and we study the estimation error. Finally, we propose a goodness-of-fit test based on entropy and give two motivations for it. The theoretical results are supported by specific numerical examples.

Asymptotic Distributions of Covering and Separation Measures on the Hypersphere.
*Discrete & Computational Geometry, ??*, ??.

(2022).
Computing the Asymptotic Distribution of Second-order $U$- and $V$-statistics.
*Computational Statistics and Data Analysis, 174*, 107437.

(2022).
Randomness, Emergence and Causation: A Historical Perspective of Simulation in the Social Sciences.
*Complexity and Emergence, Lake Como School of Advanced Studies, Italy, July 22–27, 2018*, edited by S. Albeverio, E. Mastrogiacomo, E. Rosazza Gianin and S. Ugolini, Springer Proceedings in Mathematics & Statistics 383, Springer, pp. 163-195.

(2022).
Asymptotic Properties of the Plug-in Estimator of the Discrete Entropy under Dependence.
*IEEE Transactions on Information Theory, 67*(12), 7659-7683.

(2021).
On the quest for defining organisational plasticity: a community modelling experiment.
*Evidence-based HRM: a Global Forum for Empirical Scholarship, 9*(2), 126-138.

(2020).