L'offerta formativa si svolge prevalentemente al primo anno da novembre a maggio.
Consiste in corsi obbligatori e opzionali organizzati su base annuale o biennale, laboratori e cicli di seminari, in alcuni casi comuni ai due curriculum. Tutti i corsi sono in inglese.
Giorni, orari e aule sono nel calendario al link CALENDARIO CORSI nella colonna destra.
I syllabus dei corsi sono nella colonna destra.
In caso di informazioni mancanti si prega di contattare i docenti.
CURRICULUM DI GEOGRAFIA ECONOMICA E STATISTICA TERRITORIALE | CORSI OBBLIGATORI
Data sources and indication theory | Teacher: Salvati
Number of hours: 8 | Period: 15-22 Jan | Type: Ex-cathedra
The course aims at introducing participants to the collection, management and elementary analysis of multiple data sources using spreadsheets. A taxonomy of data sources will be provided and information and tools to delineate precision, reliability and suitability. Based on examples and case studies, participants will learn theoretically and practically the specificities of official statistics and ‘non-statistical’ sources. Through a joint forum with data producers (ISTAT) and a take-home practical application, basic notions of indication theory will be offered moving from data management to the construction of indicators and composite indexes.
Applied Econometrics | Teacher: Mattera
Number of hours: 15 | Period: 5 Dec-9 Jan | Type: Ex-cathedra
The course aims to provide a solid introductory foundation in econometrics theory both considering cross-sectional data and time series analyses. It will present conceptual and practical tools, instrumental in the field of regional and territorial analysis on one hand and economic and financial forecasting on the other hand. During the classes, we will use the R software.
Key Texts & Thinkers in Economic Geography | Coord. by C. Di Feliciantonio
Number of hours: 16 | Period: 10 Dec 2024 – 20 May 2025 | Type: Workshop
This course will invite students to critically engage with some milestone texts and leading authors in contemporary economic geography. Beyond strengthening participants’ knowledge of fundamental concepts and contributions, the course aims at developing/reinforcing their public speaking skills and confidence together with their analytical skills. For each session one student will be designated to introduce the reading(s). The introduction will be followed by a debate under the coordination of a senior academic.
Economic Geography Seminars | Coord. by C. Di Feliciantonio & F. Celata
Number of hours: 10 | Period: 6 Feb-22 May 2025 | Type: Seminars
Invited seminars on some of the key emerging research themes and methodologies in economic, environmental, social and urban geography, aimed to engage participants with more experienced scholars, and to introduce them to some of the most recent theoretical and methodological advancements in the field.
PhD candidates partecipate and presents their research in workshops organized in collaboration with other PhDs (see the link in the right column).
Qualitative Research Methods | Coord. by Di Feliciantonio
Number of hours: 20 | Period: 11 Mar-May 2025 | Type: Ex-cathedra
The course aims at introducing some of the main qualitative research methods in geography and social sciences - interviews and focus groups; participant observation and ethnography - emphasizing the importance of integrating research methods. Moreover attendants will familiarize with some key-debates concerning situated knowledge, the positionality of the researcher and ethical issues raised by different research methods.
Spatial Data Analysis Visualization & Mapping | Teachers: Celata, Martellozzo, Salvati, Sciabolazza
Number of hours: 20 | Period: 6-9 May, 3 June 2025 | Type: Laboratory
Introduction to the collection, management, analysis, mapping, modelling and visualization of spatial data in a GIS environment. Participants will learn theoretically and practically what the specificities of spatial data and methods are; how to analyze geographical patterns through mapping, spatial analysis, spatial statistics, spatial regressions; how to visualize and communicate the results effectively and reflexively; and will be introduced to examples of applications in data visualization, geospatial analysis, environmental analysis, urban analysis, statistics and economics.
Community and diverse economies research and practice | Teacher: Katherine Gibson (University of Sydney)
Number of hours: 15 | Period: 27-29 May 2025 | Type: Workshop
During her visiting at Sapienza Università di Roma, Prof. Katherine Gibson, internationally known for her research on rethinking economies as sites of ethical action, is going to lead the short course ‘Community and diverse economies research and practice’, on May 27-29 2025. The course will focus on the theoretical and methodological foundations of post-capitalist and diverse economies research and practice.
Publication Strategies & Academic Writing | Teachers: Celata, Salvati, Bianchi, Filippetti, Di Feliciantonio, Incelli
Number of hours: 8 | Period: May 2025 | Type: Seminars
The workshop aims to assist Phd candidates in defining their research and publication strategies. The first session will provide a map of publishers, scientific journals, peer-review and evaluation systems, and of contemporary publication practices, as well as of funding opportunities for postdocs, in the main disciplinary fields of the PhD: Economic Geography, Statistics, Finance. The second session will be a workshop in academic writing.
CURRICULUM DI GEOGRAFIA ECONOMICA E STATISTICA TERRITORIALE | CORSI OPZIONALI
Statistical Inference | Teachers: Di Cecco/Tancredi
Number of hours: 24 | Period: 6 Nov-13 Dec | Type: Ex-cathedra/Laboratory
Part 1: Statistical models and uncertainty in inference. The Bayesian and frequentist paradigms. Likelihood: observed and expected quantities, exact properties. Invariance properties. Likelihood and sufficiency. Likelihood inference procedures. Consistency of the maximum likelihood estimator. First-order asymptotics and related inference procedures. Profile likelihood. Non-regular models. Misspecification. Composite likelihood. Conditional and marginal likelihood. Intractable models: synthetic likelihood. Part 2: Linear models: OLS. Normal distribution theory. Omitted variables bias. Model checking. Model building. Exponential family models. Generalized linear models. Probit and logit models. Count data. Overdispersion. Log-liner models.
Data structures and algorithms with R | Teacher: Arcagni
Number of hours: 15 | Period: 27-31 Jan 2025 | Type: Laboratory
This course is about the advanced methodologies in R programming. It introduces Visual Studio Code as an alternative to RStudio, providing flexibility in the development environment. Part 1: data manipulation, data structures, and object-oriented programming. Practical examples include sparse matrices, complex networks, and graph theory. Part 2: simulations and computational techniques, including simulation methodologies, optimization techniques, solving equations, numerical integration, and an introduction to backtracking algorithms.
Multivariate Statistics with R | Teacher: Liseo
Number of hours: 20 | Period: 20 Jan–13 Feb 2025 | Type: Ex-cathedra & Laboratory
Multivariate Data, Types of data and analyses, Data visualization with R - Principal Component Analysis (PCA) - Correspondence analysis (CA), Contingency tables, Interpretation and relationship of CA to PCA, - Factor Analysis (FA), The k-factor analysis model, Relationship of EFA to PCA, - Cluster Analysis, Classification VS Clustering, Measures of similarity or dissimilarity, Non-hierarchical and Hierarchical methods - Regularization techniques (Lasso and Ridge), K nearest neighborhood - Practical exercises with R.
Spatial Econometrics | Teacher: Martini
Number of hours: 15 | Period: 12-16 May 2025 | Type: Laboratory
The aim of the course is to provide students with a comprehensive understanding of spatiality, its associated challenges, and the most commonly used models and analysis techniques in spatial econometrics. Special attention will be given to post-estimation and result interpretation.
Missing Values in Data Mining | Teacher: Vitale
Number of hours: 12 | Period: 20-23 May 2025 | Type: Laboratory
This course aims to provide a theoretical and practical introduction to multiple imputation, a modern statistical technique for handling missing data which has become increasingly popular because of its generality and recent software developments.
CURRICULUM DI GEOGRAFIA ECONOMICA E STATISTICA TERRITORIALE | CORSI OPZIONALI ORGANIZZATI DA ALTRI DOTTORATI SAPIENZA
- Public Policy Analysis (PhD DiSSE) | Teachers: d’Albergo & Moini | Number of hours: 10 | Period: Dec-Jan | Type: Ex-cathedra.
- Causal Inference with Spatial Data (PhD SESS) | Teacher: Sciabolazza | Number of hours: 10 | Period: 2026 | Type: Laboratory.
- Methods of Evaluation & Microeconometrics (PhD DiSSE) | Teachers: Cerqua, Belloc, Natticchioni | Number of hours: 15 | Period: Mar-Jun | Type: Ex-cathedra.
- Advanced Evaluation Methods (PhD DiSSE) | Teacher: Cerqua | Number of hours: 10 | Period: Apr-Jun | Type: Ex-cathedra.
- Economics of Innovation (PhD Eco) | Teachers: Guarascio, Cirillo et al. | Number of hours: 10 | Period: May | Type: Ex-cathedra
- Development economics (PhD Disse/Sess) | Teachers: Montalbano, Di Maio | Number of hours: 20 | Period: May | Type: Ex-Cathedra.
- Space, cultures and politics in contemporary society (PhD DiSSE) | Teacher: Galdini et al.
- Public engagement in scientific research (PhD DiSSE) | Teacher: Manuela Perrotta (Queen Mary).
CURRICULUM DI MODELLI E METODI MATEMATICI E STATISTICI PER L’ECONOMIA E LA FINANZA | CORSI OBBLIGATORI
Mathematical Finance | Teacher: Ceci
Number of hours: 18 | Period: 14-31 Jan | Type: Ex-cathedra
The course aims to provide the tools of probability and continuous-time stochastic processes necessary to deal with the most used mathematical models in finance. It will focus on stochastic calculus for diffusion and jump-diffusion processes and their applications in finance. In particular, hedging and pricing of derivatives will be discussed.
Computational Tools for Finance | Teacher: Martire
Number of hours: 15 | Period: 13 Feb-13 Mar 2025 | Type: Laboratory
This course will cover a theoretical introduction to artificial neural networks. After the introduction of the Python language, this will be used to design neural networks to numerically solve partial differential equations and to show use in the financial option pricing framework.
Networks, Complex Networks, and Decision Problems on Networks | Teacher: Ricca
Number of hours: 12 | Period: 18-27 Feb | Type: Laboratory
Graph, networks, and networks optimization models. Complex Networks and structure measures: density and connectivity, centrality, concentration, assortativity, centrality measures based on flows. Applications to decision problems in Economics, Logistics and Finance.
Deep Learning | Teacher: Panero
Number of hours: 16 | Period: 19 Apr - 15 May 2025 | Type: Laboratory
This course is about deep learning, covering fundamental concepts of deep learning and neural networks, design of neural network architectures, optimisation methods for training neural networks, and neural networks design for particular purposes such as image recognition, sequence modelling and natural language processing. The labs of the course will be carried out in Python, using TensorFlow, on the Google Colab environment.
Financial Risk Modeling and Forecasting Using Quantile Regression | Teachers: Foroni & Merlo
Number of hours: 12 | Period: 13-17 May | Type: Ex-cathedra
This 12-hour course aims to offer an introductory overview of the quantile regression framework and its applications in finance and risk analysis. This course will also present the most prominent quantile-based models for forecasting and evaluating tail risk measures. A practical session is included to implement the methodologies discussed in R.
Publication Strategies & Academic Writing | Teachers: Celata, Salvati, Bianchi, Filippetti, Di Feliciantonio, Incelli
Number of hours: 8 | Period: May | Type: Seminars
The workshop aims to assist Phd candidates in defining their research and publication strategies. The first session will provide a map of publishers, scientific journals, peer-review and evaluation systems, and of contemporary publication practices, as well as of funding opportunities for postdocs, in the main disciplinary fields of the PhD: Economic Geography, Statistics, Finance. The second session will be a workshop in academic writing.
Fractional Calculus | Teacher: Frezza
Number of hours: 10 | Period: 2026 | Type: Ex-cathedra
This course will cover both a theoretical introduction to fractional calculus and its application to finance. After the introduction of the concepts of fractional derivatives and fractional integrals, the course will be devoted to analyze the main (multi)fractional processes used in finance to model financial data.
CURRICULUM DI MODELLI E METODI MATEMATICI E STATISTICI PER L’ECONOMIA E LA FINANZA | CORSI OPZIONALI
Mathematics | Teacher: Palestini
Number of hours: 10 | Period: 5 Nov-3 Dec | Type: Ex-cathedra
Functions of several variables: Domain of the functions of 2 real variables, level curves, first order partial derivatives, stationary point and their nature, optimization problems with applications in Microeconomics. Differential equations: Ordinary differential equations, basic methods of integration, qualitative analysis on the plane. Dynamical systems: Stationary points, time elimination method, solution curves.
Statistical Inference | Teachers: Di Cecco/Tancredi
Number of hours: 24 | Period: 6 Nov-13 Dec | Type: Ex-cathedra/Laboratory
Part 1: Statistical models and uncertainty in inference. The Bayesian and frequentist paradigms. Likelihood: observed and expected quantities, exact properties. Invariance properties. Likelihood and sufficiency. Likelihood inference procedures. Consistency of the maximum likelihood estimator. First-order asymptotics and related inference procedures. Profile likelihood. Non-regular models. Misspecification. Composite likelihood. Conditional and marginal likelihood. Intractable models: synthetic likelihood. Part 2: Linear models: OLS. Normal distribution theory. Omitted variables bias. Model checking. Model building. Exponential family models. Generalized linear models. Probit and logit models. Count data. Overdispersion. Log-liner models.
Applied Econometrics | Teacher: Mattera
Number of hours: 15 | Period: 5 Dec-9 Jan | Type: Ex-cathedra
The course aims to provide a solid introductory foundation in econometrics theory both considering cross-sectional data and time series analyses. It will present conceptual and practical tools, instrumental in the field of regional and territorial analysis on one hand and economic and financial forecasting on the other hand. During the classes, we will use the R software.
Data structures and algorithms with R | Teacher: Arcagni
Number of hours: 15 | Period: 27-31 Jan 2025 | Type: Laboratory
This course is about the advanced methodologies in R programming. It introduces Visual Studio Code as an alternative to RStudio, providing flexibility in the development environment. Part 1: data manipulation, data structures, and object-oriented programming. Practical examples include sparse matrices, complex networks, and graph theory. Part 2: simulations and computational techniques, including simulation methodologies, optimization techniques, solving equations, numerical integration, and an introduction to backtracking algorithms.
Multivariate Statistics with R | Teacher: Liseo
Number of hours: 20 | Period: 20 Jan–13 Feb 2025 | Type: Ex-cathedra & Laboratory
Multivariate Data, Types of data and analyses, Data visualization with R - Principal Component Analysis (PCA) - Correspondence analysis (CA), Contingency tables, Interpretation and relationship of CA to PCA, - Factor Analysis (FA), The k-factor analysis model, Relationship of EFA to PCA, - Cluster Analysis, Classification VS Clustering, Measures of similarity or dissimilarity, Non-hierarchical and Hierarchical methods - Regularization techniques (Lasso and Ridge), K nearest neighborhood - Practical exercises with R.
Missing Values in Data Mining | Teacher: Vitale
Number of hours: 12 | Period: 20-23 May 2025 | Type: Laboratory
This course aims to provide a theoretical and practical introduction to multiple imputation, a modern statistical technique for handling missing data which has become increasingly popular because of its generality and recent software developments.
Machine Learning for Econometrics (PhD Eco) | Teachers: Bloise, Tancioni, et al. | Number of hours: 15 | Period: Mar-Jun | Type: Laboratory.
ORGANIZZAZIONE DIDATTICA
I dottorandi decidono con i propri tutor quali corsi frequentare tra quelli organizzati dal Dottorato o da altri Dottorati Sapienza. Gli studenti possono inoltre concordare con i propri tutor la frequenza ad eventuali altri corsi di dottorato o avanzati organizzati da altri atenei, inclusa la partecipazione a summer school o simili.
Il numero di ore dei corsi obbligatori per ciascun dottorando è di circa 100. Il numero di ore dei corsi opzionali è in genere tra le 50 e le 100 ore. La frequenza dei corsi è obbligatoria ed esclusivamente in presenza.
PROGETTO DI RICERCA E AMMISSIONE AL SECONDO ANNO
I dottorandi al primo anno dovranno inviare entro a settembre un outline del proprio progetto di ricerca, che dovrà essere predisposto e approvato dai propri tutor. Il documento (minimo 2.000 parole) include: 1) il titolo preliminare, 2) una breve descrizione della ricerca, i suoi obiettivi, il quadro teorico, i risultati attesi e il loro contributo all'avanzamento dello stato dell'arte della ricerca sull'argomento, 3) la descrizione preliminare delle metodologie di ricerca, 4) una struttura preliminare della tesi, compreso il suo formato (formato 'book' o tre articoli), 5) il/i nome/i del/i potenziale/i supervisore/i.
Sulla base dell'outline del progetto di ricerca, nel mese di ottobre vengono organizzati uno o più workshop pubblici in cui i dottorandi presentano e discutono la loro ricerca con il collegio di dottorato e con gli altri dottorandi.
L'ammissione al secondo anno viene deliberata nel mese di ottobre sulla base dei corsi frequentati, dei risultati di eventuali esami o assignment, e del loro progetto di ricerca.