Economics, Networks and Business Analytics

Econometrics II

- Microdata and Heterogeneity
- Potential Outcome Framework
- Difference-in-difference and treatment effects
- Linear and Static Panel Data Models
- Linear and Dynamic Panel Data Models
- Non-Linear Models
- Categorical variables and count data
- Multinomial models

Econometrics I

- Review of Asymptotic Theory
- Theory and Algebra of OLS
- Inference, non-spherical Errors and Clustering
- Structural Models, Identification and Causality
- Simultaneous Equation Models, 2SLS and 3SLS
- Introduction to M-Estimation
- Generalized Method of Moments
- Maximum Likelihood Estimation

Decision-Making in Economics and Management

The main goals of the course are:
(1) to take economic theories and methodologies out into the world, applying them to interesting questions of individual behavior and societal outcomes;
(2) to develop a basic understanding of human psychology and social dynamics as they apply to marketing contexts;
(3) to become familiar with the major theory and research methods for analyzing consumer behavior;
(4) to develop market analytics insight into consumer actions.

Data Science Lab

The aim of this class is to provide students with R language fundamentals and basic sintax. In particular, lessons will cover the following topics:

- Overview of R features
- The basics (vectors, matrices, objects, manipulation, basic statements)
- Reading data from files
- Probability distributions
- Basic statistical models
- Graphical procedures
- R packages overview

Critical Thinking (long seminar without exam)

Constructing and evaluating arguments is fundamental in all branches of science, as well as in everyday life. The course provides the basic skills and tools to recognize correct forms of inference and reasoning, detect the unsound or fallacious ones, and assess the strength of various kinds of argument. The toolbox includes elementary deductive logic, patterns of inductive and abductive inference, the basics of statistical and probabilistic reasoning, and the analysis of heuristics and biases in cognitive psychology.

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.

Business Model for Emerging Markets

Teaching contents:

1. The economy of the intangibles
2. Manufacturing and robot
3. Strategy and business model
4. How to model a business
5. How to model a business in a complex scenario
6. What make market emerging? Not only new lands.
7. The Blockchain technology and the future
8. Initial Coins Offering (ICO) compressed between Business plan and White paper
9. Possible value of Blockchain technology for Small and medium Italian sized business
10. A global value chain approach to protect and foster strategic identity

Analytics in Economics and Management

The aim of this course is to teach students how to produce a research paper in economics and management using hands-on empirical tools for different data structures. We will bridge the gap between applications of methods in published papers and practical lessons for producing your own research. After introductions to up-to-date illustrative contributions to literature, students will be asked to perform their own analyses and comment results after applications to microdata provided during the course