Courses

ECON 2013-2014

Courses found: 34


Advanced Topics in Econometrics

The main goal of this course is to provide an introduction to/review of the fundamental theoretical concepts and applications of modern econometric techniques used in empirical social sciences.

TOPICS covered in the course include:

1) Conventional Methods to Estimate Causal Effects: OLS and IV
2) The Angrist-Imbens-Rubin approach (LATE-IV)
3) Further IV issues and Regression Discontinuity Design (RDD)
4) Matching methods for the estimation of causal effects
5) Differences-in-Differences estimation
30 Hours
Professors / Lecturers: Paolo Pinotti, Università Bocconi
Available for Curricula: Economics  (CORE) ;


Basic Numerical Linear Algebra

The course is aimed to introduce the basic notions about vector spaces, vectors, matrices, and norms, along with the basic numerical methods concerning the solution linear systems. In particular: direct methods for square linear systems and conditioning analysis; direct methods for solving over-determined linear systems in the least square sense, with applications. The course also provides an introduction to Matlab, which is used for implementing the methods.
20 Hours
Professors / Lecturers: Luigi Brugnano, Univ. Firenze
Available for Curricula: Computer Science  (INT) ; Systems Science  (INT) ; Image Analysis  (INT) ; Management Sciences  (INT) ; Economics  (INT) ;


Computational Contact and Fracture Mechanics

This course provides a general overview on the theories of contact and fracture mechanics, relevant for a wide range of disciplines ranging from materials science to engineering and geophysics. Introducing their theoretical foundations, the physical aspects of the resulting nonlinearities induced by such phenomena are emphasized. Numerical methods for their approximate solution are also presented, together with a series of applications to real case studies.
The course covers the following topics:
I. Contact mechanics
A. The Hertzian contact between smooth spheres
B. The Cattaneo-Mindlin theory for frictional contact
C. Numerical methods for the treatment of the unilateral contact constraint (the penalty method
and Lagrange multipliers in FEM, the active set strategy in BEM)
D. Contact between rough surfaces: statistical and numerical methods

II. Fracture mechanics
A. Fundamentals of linear elastic fracture mechanics (LEFM), stress-intensity factors
B. Strength and toughness of materials, criteria for crack propagation
C. Examples in LEFM solved with the use of the finite element method
D. Nonlinear fracture mechanics (NLFM): the cohesive zone model (CZM)
E. Numerical implementation of the CZM in the finite element method
F. Applications of NLFM to materials science, retrofitting of civil/architectonic structures,
composite materials
20 Hours
Professors / Lecturers: Marco Paggi
Available for Curricula: Computer Science  (SUGG) ; Systems Science  (ADV) ; Image Analysis  (SUGG) ; Management Sciences  (SUGG) ; Economics  (SUGG) ;


Computational Finance

In the field of quantitative finance, one of the most challenging tasks is the gap between theoretical models and the actual software implementation. Cross some different areas (derivatives evaluation, risk management, accounting issues) several problems arise: discretization, analytical approximation, montecarlo simulation vs. numerical probability, optmization and so on. After a short overview of the main financial areas, the course aims to give some insights on these topics, with a special focus on the risk management current hard problems and the related software algorithms.
20 Hours
Professors / Lecturers: Michele Bonollo, Credito Trevigiano
Available for Curricula: Computer Science  (ADV) ; Systems Science  (ADV) ; Management Sciences  (ADV) ; Economics  (ADV) ;


Computer Programming and Methodology

This course aims at introducing to students principles and methodologies of computer programming. Emphasis is on good programming style, techniques and tools that allow efficient design, development and maintenance of software systems. The course focuses on the design of computer applications drawing attention to modern software engineering principles and programming techniques, like object-oriented design, decomposition, encapsulation, abstraction, and testing. A significative case study is used to allow students to experiment with the principles and techniques considered in this course. Depending on the background of the class, Java, C++, and/or Python are considered in the course.
20 Hours
Professors / Lecturers: Michele Loreti, Univ. Firenze
Available for Curricula: Computer Science  (INT) ; Systems Science  (INT) ; Image Analysis  (INT) ; Management Sciences  (INT) ; Economics  (INT) ;


Data Science with Complex Networks

Complex Systems are everywhere and in the era of massive production of electronic data coming from all sort of devices it is of crucial importance to have the right tools to manage and extract from them all the valuable information. To this aim during this course we will develop both the basic theoretical tools and the practical coding technics to tackle all sort of complex systems, ranging from Trade and Financial Networks, to the World Wide Web and the Social Networks. In particular Complex Networks Theory proved to be successful in the process of handling this enormous quantity of data and in order to apply these concepts to the various cases it is crucial to define a clear strategy and guidelines to represent the system data in the shape of a network. Using the Python scripting language we will introduce state of the art methods and algorithms to cope with some reference dataset.
20 Hours
Professors / Lecturers: Alessandro Chessa / Guido Caldarelli
Available for Curricula: Computer Science  (ADV) ; Systems Science  (ADV) ; Image Analysis  (ADV) ; Management Sciences  (ADV) ; Economics  (ADV) ;


Econometrics I

This course presents a comprehensive treatment of econometric methods for linear models and assumes working knowledge of undergraduate econometrics basic linear algebra, basic probability theory, and statistics that are covered in the pre-courses.
40 Hours
Professors / Lecturers: Cristina Tealdi / Marco Leonardi, Universita' Cattolica di Milano
Available for Curricula: Management Sciences  (ADV) ; Economics  (CORE) ;


Econometrics II

I - Overview

This part of the course is designed to provide an introduction to models with limited dependent variables: discrete choice models, truncation, censoring and sample selection. The models presented here require a working knowledge of multivariate calculus, linear algebra, maximum likelihood and least squares estimation. Emphasis is on applications and use rather than methods and each topic is addressed with in-depth Stata examples.

Discrete Choice Models (DCM)
- DCM.1 The linear probability model
- DCM.2 Binary Logit and probit models
- DCM.3 Maximum Likelihood: estimation and test
- DCM.4 Multinomial and ordered response models

Truncation, censoring and sample selection (TCS)
- TCS.1 Censoring and truncation
- TCS.2 Corner solution responses
- TCS.3 Censoring and Sample selection

II - Overview

The course aims at introducing linear models for (stationary) panel data, i.e. repeated observations on the same statistical units. This kind of data are nowadays widely used in several fields so that the knowledge of the
appropriate econometric techniques is necessary for applied researchers. At first, static models will be introduced and the most widely used estimators - alongside with the hypotheses needed for their consistency - will be discussed. Subsequently, dynamic models will be presented, with a necessary digression about GMM (Generalized Method of Moments) estimator, which proves to be the appropriate estimator for this kind of models.

Contents of the classes

Class 1: Static Linear Panel data models. Introduction to Panel Data and main estimators.
Class 2: Static Linear Panel data models. Test of hypotheses and different variance structures. Generalized Method of Moments Estimation. Introduction.
Class 3: Generalized Method of Moments Estimation. Test of hypotheses. Dynamic Linear Panel data models. Introduction and bias of standard estimators.
Class 4: Dynamic Linear Panel data models. Arellano-Bond and Blundell-Bond estimators.
36 Hours
Professors / Lecturers: Carla Rampichini, Università degli Studi di Firenze / Valentina Tortolini / Luigi Benfratello, Università degli Studi di Napoli Federico II
Available for Curricula: Management Sciences  (ADV) ; Economics  (CORE) ;


Economic Growth & Development

The main goal of the course is to introduce students to the Neoclassical growth model, the overlapping-generations model and endogenous technological change. The course will provide the basic tools required to study long run growth and provide some answers to the fundamental question of why economies grow and why some countries are much richer than others. The main topics will be the following.
Stylized facts of economic growth and development. Introduction to the Solow growth model. Neoclassical growth. Overlapping generations and dynamic efficiency.
Neoclassical endogenous growth: capital accumulation, externalities and human capital. The Shumpeterian approach to economic growth. The effect of institutions on long run growth.
30 Hours
Professors / Lecturers: Massimiliano Onorato
Available for Curricula: Economics  (CORE) ;


Endogenous Innovation and Credit Cycles (long seminar)

TBD
6 Hours
Professors / Lecturers: Kiminori Matsuyama (Northwestern University)
Available for Curricula: Economics  (ADV) ;


Essentials of Calculus

The course aims at recalling the fundamental concepts of static optimization.
The basics of calculus (in one and more variables) are supposed to be known.
In particular the course will deal with the following topics.
- Functions of one variable: drawing graph of functions, application of continuity and derivatives to constrained and unconstrained optimization, differentiation and maximization of integral functions.
- Functions of several variables: partial derivatives, methods for constrained and unconstrained optimization, optimization of implicit and integral functions.
20 Hours
Professors / Lecturers: Alexander Petersen / Orion Penner
Available for Curricula: Computer Science  (INT) ; Systems Science  (INT) ; Image Analysis  (INT) ; Management Sciences  (INT) ; Economics  (INT) ;


Ethics and Research: Objectivity, Neutrality and Values in Science (long seminar)

The idea that science – “pure” science, that is, as opposed to “applied” science, that is technology – is a morally neutral enterprise is often presented as a matter of fact. It is obviously so, it is argued, because the task of science, as we understand it, is that of explaining phenomena, not that of telling how phenomena should be. And it is importantly so, because any interference of values (broadly conceived) in the scientific discourse would entail the subordination of the search for truth, which is taken to be as the aim of science, to politics, religion, or any metaphysical framework. The well known case of Galileo (1564-1642), or that of Lysenko (1598-1976) – however different they were – are all too clear warnings about what might happen if scientific research were to depend on values alien to it.
In this series of seminars students will be exposed to and challenged by a different view. According to a growing number of philosophers of science, the commitment to values is inescapable for scientists, for values are indeed part and parcel of scientific research. In order to prevent cases such as those of Galileo and Lysenko from happening again, however, the often conflated ideas of “objectivity” and “neutrality” (scientific knowledge must be neutral in order to be objective, that is) must be clearly distinguished. Furthermore, it may be argued that any attempt to free scientists from any moral responsibility would not only prevent them from achieving the independence they rightly strive for, but would seriously challenge it.
Structure of the “long seminar” (preliminary): 2-3 introductory lectures and a series of seminars (presentation of papers by students + discussion).
10 Hours
Professors / Lecturers: Stefano Gattei
Available for Curricula: Computer Science  (SUGG) ; Systems Science  (SUGG) ; Image Analysis  (SUGG) ; Management Sciences  (SUGG) ; Economics  (SUGG) ; Management and Development of Cultural Heritage  (SUGG) ; Political History  (SUGG) ;


Foundations of Probability Theory

This course aims at introducing from an advanced point of view the fundamental concepts of probability theory. Moreover, various forms of convergence are introduced and studied and some important limit results and tools (such as the Fourier transform/characteristic function) are illustrated. At the beginning of the course some elements of measure theory and integration theory (the Lebesgue integral) are given.
Some proofs are sketched or omitted in order to have more time for examples, applications and exercises.
20 Hours
Professors / Lecturers: Irene Crimaldi
Available for Curricula: Computer Science  (INT) ; Systems Science  (INT) ; Image Analysis  (INT) ; Management Sciences  (INT) ; Economics  (INT) ;


Game Theory

Mechanism Design. Revelation principle, Dominance and Nash Implementation.
Strategic and Axiomatic Bargaining.
Asymmetric Information and Optimal Contracts. Moral Hazard and Adverse Selection models.
Signaling and Screening Models. Applications.
Static games of complete information: definition of a game; normal form representation; strongly and weakly dominated strategies; Nash Equilibrium (NE); mixed strategy equilibrium. Applications of NE and introduction to market competition; Cournot competition; Bertrand competition; externalities; public goods.

Dynamic games of complete information: definition of a dynamic game; extensive form representation; perfect and imperfect information; Backward Induction equilibrium; Subgame Perfect equilibrium. Repeated games: Definition; one-shot deviation property; folk theorem; application to Rubinstein bargaining. Static games of incomplete information: Bayesian games; Bayesian Nash equilibrium. Dynamic games of incomplete information: perfect Bayesian equilibrium; signalling games, cheap talk.
30 Hours
Professors / Lecturers: Nicola Dimitri, Univ. Siena
Available for Curricula: Computer Science  (ADV) ; Systems Science  (ADV) ; Management Sciences  (ADV) ; Economics  (CORE) ;


Innovation and Industrial Dynamics

Size and Growth of Economic systems. The growth of Business Firms: theoretical framework. Gibrat legacy.
20 Hours
Professors / Lecturers: Massimo Riccaboni
Available for Curricula: Economics  (CORE) ;


Intellectual Property and Management of Research (long seminar)

1. A short introduction to the funding schemes of Horizon 2020
1.1 Support of frontier research by the European Research Council:
Starting and Advanced Grants, Proof of Concept, Synergy Grant
1.2 Support of future and emerging technology: FET flagships initiatives
1.3 Training and career perspectives of researchers: Marie Curie actions

2. Other funding opportunities in EU and USA: the Alexander von Humboldt Foundation and the Deutscher Akademischer Austausch Dienst for Germany; Fulbright scholarships for USA; the Royal Society in UK; joint cooperation projects with France.
3. How to write a budget of a proposal.
4. How to manage your granted project.

Some seminars by invited experts will be offered.
15 Hours
Professors / Lecturers: Marco Paggi
Available for Curricula: Computer Science  (SUGG) ; Systems Science  (SUGG) ; Image Analysis  (SUGG) ; Management Sciences  (SUGG) ; Economics  (SUGG) ; Management and Development of Cultural Heritage  (SUGG) ; Political History  (SUGG) ;


Introduction to Mathematical Statistics and Stochastic Processes

This course aims at introducing the concept of statistical inference and the notion of stochastic process. Some basics of mathematical statistics are given and Markov chains, Poisson process and martingales are studied.
Some proofs are sketched or omitted in order to have more time for examples, applications and exercises.
Prerequisites: The topics of “Foundations of Probability Theory” are supposed to be known.
20 Hours
Professors / Lecturers: Irene Crimaldi
Available for Curricula: Computer Science  (INT) ; Systems Science  (INT) ; Image Analysis  (INT) ; Management Sciences  (INT) ; Economics  (INT) ;


Introduction to Networks Theory

TBD
10 Hours
Professors / Lecturers: Guido Caldarelli
Available for Curricula: Computer Science  (INT) ; Systems Science  (INT) ; Image Analysis  (INT) ; Management Sciences  (INT) ; Economics  (INT) ; Management and Development of Cultural Heritage  (INT) ;


Introduction to Stochastic Control Theory and Applications

Aims
The course is to provide students with an overview of the main methods and recent developments in the area of stochastic control and their applications to economics.

Contents
Classical approach to stochastic control problem by dynamic programming methods. Viscosity solutions and stochastic control.
20 Hours
Professors / Lecturers: Andrea Vindigni / Simone Scotti, Università di Torino
Available for Curricula: Systems Science  (ADV) ; Management Sciences  (ADV) ; Economics  (ADV) ;


Macroeconomics I

Search models. Equilibrium search and matching. Two-sided search model. Efficiency wages. Implicit contacts. Insider-outsider models. Traditional Keynesian Theories of Fluctuations. The Lucas Imperfect-Information Model and The Lucas Critique. The New Keynesian Economics. Consumption. Investment.
40 Hours
Professors / Lecturers: Davide Ticchi / Cristina Tealdi
Available for Curricula: Economics  (CORE) ;


Macroeconomics II

Dynamic stochastic general equilibrium models. The Real Business Cycle Theory. International real business Cycles. The New Keynesian Economics. Monetary policy.
20 Hours
Professors / Lecturers: Oded Stark, Universities of Bonn, Klagenfurt, Tuebingen, Vienna, and Warsaw; Georgetown University/Davide Ticchi
Available for Curricula: Economics  (CORE) ;


Management

Applications of quantitative techniques to managerial decisions (data-driven decision making). Topics include applications of data mining, machine learning, statistical models, predictive analytics, econometrics, optimization, risk analysis, decision theory, data visualization and business communication in finance, marketing, operations, R&D, business intelligence and other business areas generating and consuming large amounts of data.
10 Hours
Professors / Lecturers: Massimo Riccaboni
Available for Curricula: Computer Science  (ADV) ; Systems Science  (ADV) ; Management Sciences  (ADV) ; Economics  (ADV) ;


Management (Basics)

TBD
10 Hours
Professors / Lecturers: Massimo Riccaboni
Available for Curricula: Computer Science  (INT) ; Systems Science  (INT) ; Management Sciences  (INT) ; Economics  (INT) ; Management and Development of Cultural Heritage  (INT) ;


Microeconomics

The course covers classical decision theory and partial equilibrium analysis. It aims to provide a smooth transition from undergraduate- to graduate-level Microeconomics, and to build a solid foundation for future study and research.
40 Hours
Professors / Lecturers: Nicola Dimitri, Università di Siena
Available for Curricula: Economics  (CORE) ;


Networks Theory

Course description: Basic of Graph Theory: degree, clustering, connectivity, assortativity, communities. Analysis of Complex Networks, datasets and software. Community Detection, Modularity, Spectral Properties. Fractals, Self-Organised Criticality, Scale Invariance. Random Graph, Barabasi Albert Model, Fitness model, Small world. HITS Algorithm and PageRank. Real instances of Complex Networks in Biology and Social Sciences. Board of Directors, Ownership Networks, measures of Centrality and Control. World Trade Web, Minimal Spanning Trees, Competition and Products spaces. Prerequisites: Linear algebra and matrix computation, calculus and mathematical analysis.
20 Hours
Professors / Lecturers: Guido Caldarelli / Massimo Riccaboni
Available for Curricula: Computer Science  (ADV) ; Systems Science  (ADV) ; Image Analysis  (ADV) ; Management Sciences  (ADV) ; Economics  (ADV) ;


Numerical Methods for the Solution of PDEs

This course introduces PhD students to numerical techniques for the approximate treatment of linear partial differential equations (PDEs) governing physical, engineering and financial problems. The theoretical fundamentals of the finite element method are introduced step-by-step in reference to exemplary model problems related to heat conduction, linear elasticity and pricing of stock options in finance. Special attention is given to the finite element technology and to the implementation of the weak forms into a research code for fast intensive computations.
The course covers the following topics:
I. Heat conduction
A. Strong and weak forms
B. Finite element approximation
C. Isoparametric shape functions and numerical integration
D. Transient analysis
E. Numerical implementation
F. Examples
II. Option pricing in finance
A. The Black-Scholes-Merton model: strong and weak forms
B. Finite element approximation
C. Numerical implementation
D. Examples
III. Linear elasticity
A. The minimum potential energy theorem
B. The displacement finite element method
C. Finite element discretization in 2D and 3D, numerical integration
D. Examples in materials science and structural mechanics
20 Hours
Professors / Lecturers: Marco Paggi
Available for Curricula: Computer Science  (ADV) ; Systems Science  (ADV) ; Image Analysis  (ADV) ; Management Sciences  (ADV) ; Economics  (ADV) ;


Optimal Control

Discrete-time optimal control: dynamic programming for finite/infinite horizon and deterministic/stochastic optimization problems. LQ and LQG problems, Riccati equations, Kalman filter. Deterministic continuous-time optimal control: the Hamilton-Jacobi-Bellman equation and the Pontryagin’s principle. Examples of optimal control problems in economics.
20 Hours
Professors / Lecturers: Giorgio Gnecco
Available for Curricula: Computer Science  (INT) ; Systems Science  (INT) ; Image Analysis  (INT) ; Management Sciences  (INT) ; Economics  (INT) ;


Political Economy

TBD
40 Hours
Professors / Lecturers: Davide Ticchi/Gilles Saint-Paul (Paris School of Economics)/Massimiliano Onorato
Available for Curricula: Economics  (CORE) ;


Quantitative Finance

The course covers important topics in modern quantitative finance and risk management: efficient market hypothesis and violations, financial markets micro-structure and types of arbitrage, general principles of modelling the price dynamics of financial assets, market risk and other types of financial risks, Value-at-Risk (VaR) approach and applications, modelling of extreme events and crisis, VaR analysis for financial derivatives, copula methods,modelling of trends in time series in connection with technical analysis, and the foundations of high-frequency arbitrage trading. This course will enable the students to develop both theoretical knowledge and practical skills to analyze modern financial markets.
20 Hours
Professors / Lecturers: Roberto Reno', Universita' di Siena
Available for Curricula: Computer Science  (ADV) ; Systems Science  (ADV) ; Management Sciences  (ADV) ; Economics  (ADV) ;


Scientific Writing, Dissemination and Evaluation (long seminar)

TBD
6 Hours
Professors / Lecturers: Luca Aceto, Reykjavik University
Available for Curricula: Computer Science  (SUGG) ; Systems Science  (SUGG) ; Image Analysis  (SUGG) ; Management Sciences  (SUGG) ; Economics  (SUGG) ; Management and Development of Cultural Heritage  (SUGG) ; Political History  (SUGG) ;


States and Markets

The main topic is the political economy of state formation in historical and comparative perspective. We will read some classic works by economic historians, sociologists and political scientists and try to relate this work to the modern literature (in economics) on the political economy of state capacity. We will also briefly talk of some theories (old and new) on the origins of federalism, and try to relate this work to the literature on state formation. rules and redistribution.
20 Hours
Professors / Lecturers: Andrea Vindigni
Available for Curricula: Economics  (CORE) ; Political History  (CORE) ;


Statistics Lab

• Brief intro to R
• Creating random variables. Applications to the central limit theorem and the law of large numbers
• Descriptive statistics: (i) Representing probability and cumulative distribution functions in discrete and continuous cases; (ii) calculating mean, variance, concentration indexes, covariance and correlation.
• Inferential statistics: (i) Point estimation and properties; (ii) interval estimation and properties (iii) hypothesis testing and properties.
• Theory and applications of simple regression model (model, assumptions, estimation methods, residual diagnostics)
If time permits:
• Theory and applications of Bootstrap and Jacknife elements for simple parameters and for the regression model parameters
• Theory and applications of Logit, Probit , count data (poisson, negative binomial) regression models

Recommended prerequisites:
• Course in Foundations of Probability Theory (I. Crimaldi)
• Course in Introduction to Mathematical Statistics and Stochastic Processes (I. Crimaldi)
• Very basic R knowledge (http://www.r-project.org/)

R packages:
Boot, bootstrap, stats, base (more to come)
10 Hours
Professors / Lecturers: Rodolfo Metulini
Available for Curricula: Computer Science  (INT) ; Systems Science  (INT) ; Image Analysis  (INT) ; Management Sciences  (INT) ; Economics  (INT) ;


Time Series

TBD
20 Hours
Professors / Lecturers: Luca Gambetti (Universitat Autònoma de Barcelona)
Available for Curricula: Economics  (CORE) ;


Topics in Political Economy

Economic policy and Politics. Preferences and Institutions. Electoral competition and lobbying. Agency. Redistributive Politics: general (pension, regional transfer and unemployment insurance) and special interest politics (local public goods, lobbying and electoral competition). Political economy of regulation. Electoral Seminar series
40 Hours
Professors / Lecturers: Andrea Vindigni
Available for Curricula: Economics  (CORE) ;