Mathematics
Instructor: Arsen Palestini
Hours: 20
Mandatory: Yes |
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
Instructor: A. Tancredi e D. De Cecco
Hours: 20
Mandatory: Yes |
First part: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. The EM algorithm. Application of the EM algorithm: mixture models, hidden Markov models. Second part: Linear models: OLS. Normal distribution theory. Omitted variables bias. Model checking. Model building. Model diagnostic. Exponential family models. Generalized linear models. Probit and logit models. Count data. Overdispersion. Log-liner models. |
Macroeconomics I
Instructors: Giovanni Di Bartolomeo, Francesco Nucci
Hours: 20
Mandatory: Yes |
1) Dynamic Stochastic General Equilibrium (DSGE) models: the RBC model. First, we illustrate the standard procedure for solving non-linear, dynamic, discrete-time stochastic models. The emphasis will be on the economic blocks of the theoretical framework and the computational aspects. After deriving the equilibrium conditions of the model and the steady-state relationships among variables, we log-linearize the equilibrium equations of the system and subsequently rely on the Blanchard and Khan (1980) method to obtain the recursive relationships associated with the dynamic equilibrium (policy functions). We then analyze the model predictions by conducting impulse response and variance decomposition analyses. We will focus first on a baseline version of the Real Business Cycle (RBC). 2) The New Keynesian DSGE model. We then analyze a baseline Dynamic Stochastic General Equilibrium (DSGE) model of the New Keynesian type. In this framework, we allow for imperfect competition and nominal rigidities with the hypothesis of random price duration. After deriving the solution, we analyze the predictions of the model and discuss the propagation mechanisms featured in it. We also analyze the equilibrium dynamics under different monetary policy rules. 3) Optimal policies in DSGE New Keynesian models and open macroeconomics. This part of the course focuses on optimal policies in DSGE New Keynesian models and open macroeconomics. The normative part analyzes the sources of inefficiencies in NK models and how different policy regimes affect the optimal conduct of monetary policy. In particular, the focus will be on discretionary vs. commitment regimes. Concerning the positive part, it shows how they embed an NK model with elements of the open economy like exchange rates, trade balance, and uncovered interest parity. |
Microeconomics I
Instructor: Luca Panaccione
Hours: 20
Mandatory: Yes |
Theory of the consumer: preferences, utility, and utility maximization Theory of the producer: technology, profit, and profit maximization. Feasible allocations. Pareto optimal allocations. General Economic Equilibrium. First and Second Welfare Theorems. Existence of competitive equilibrium. General Equilibrium under Uncertainty: the Arrow-Debreu equilibrium. |
Econometrics
Instructors: Marco Ventura, Valerio Sciabolazza, Valeria Patella
Hours: 32
Mandatory: Yes |
CROSS SECTION:This part of the course presents recent advancements in the econometric literature for designing and implementing causal studies. Through lectures and hands-on sessions, students will gain a deep understanding of potential outcome models, difference-in-differences (DiD), regression discontinuity designs (RDD), and synthetic control methods. TIME SERIES ANALYSIS: Autoregressive Moving Average processes; Maximum Likelihood Estimation and Numerical Optimization; Non-stationary ARIMA processes and Stationarity tests; Stochastic Vector Processes; Estimation; Model Selection and Diagnostics; Structural VARs: Short-run Identification; Recursively Identified Models; Prediction and Impulse Responses, Variance and Historical Decompositions, Forecasts. |
Research Methods in Macroeconometrics
Instructor: Massimiliano Tancioni
Hours: 20
Mandatory: Yes |
The course provides the tools used in contemporary applied macroeconometrics. The Vector Autoregressive (VAR) model is introduced as the consistent representation of the joint data density, addressing issues of stationarity and invertibility into the Vector Moving Average (VMA) representation and its uses for prediction and structural macroeconomic analysis. Within this basic setting, the triangular factorization (Cholesky) is considered as a special case of the A-B representation of the structural VAR (SVAR) of interest. Contemporaneous and long-run identification strategies based on exclusion restrictions are exemplified, and their relation with the Instrumental Variable estimator (IV) is addressed. The Monte Carlo Markov Chain (MCMC) Bayesian estimator is introduced from the perspective of the estimation of the VAR coefficients. Identification strategies relying on theory-based sign restrictions are introduced as an alternative/complement of exclusion restrictions (in the latter case, resulting in mixed strategy based on both methods). The issue of nonlinear dynamics is addressed in the context of the BVAR by considering the time-varying coefficient SVAR (TVC-SVAR) and the Markov-Switching SVAR (MS-VAR). The local projections method is introduced as an alternative to SVARs, and the use of external instruments is described as a structural identification strategy through the IV-LP and Proxy-VAR methods. Heteroskedasticty-based VAR identification closes the module. The course takes place in 8 lectures and 4 practices (two hours each) with examples and applications using Matlab and Python. |
Research Methods in Microeconometrics
Instructors: Cerqua, Marianna Belloc, Paolo Naticchioni
Hours: 20
Mandatory: Yes |
The course introduces essential tools used in microeconometrics and techniques to estimate the causal effect of a treatment variable on an outcome variable. It presents the main policy evaluation methods, such as matching methods, diff-in-diffs estimator, the regression discontinuity design, the synthetic control method, and a brief introduction to counterfactual approaches in the absence of untreated units. These tools are widely used in all fields of microeconomics (labour and public economics, industrial economics, household economics, public policies, economics of education), and their applications are also increasing in macroeconomics fields (for instance, development economics and empiric growth). PART I: Panel data analysis - Introduction on causal inference - Assumptions about the unobserved effects and explanatory variables - Pooled OLS - Fixed effects (within) estimator: - Least squares dummy variable regression - Fixed effects estimator and measurement errors - Fixed effects estimator and lagged dependent variable - First differencing methods - Random effects estimator - The Hausman test. PART II: Policy evaluation methods - Introduction to policy evaluation methods - Matching methods - Difference-in-differences estimator - Matching difference-in-differences estimator - Regression discontinuity design - Synthetic control method - R session - Machine learning control method. |
Research Methods in Microeconomics
Instructors: Giuseppe Attanasi, Antonio Cosma, Luca Panaccione, Stefano Papa
Hours: 20
Mandatory: Yes |
Economic experiments are conducted in controlled laboratory environments to test economic theory, look for behavioral regularities, formulate new theories to explain unpredicted regularities, and make policy recommendations by testing new policies and fine-tuning existing ones. The course is an introduction to the theory and practice of experimental economics, with a look at its behavioral implications on existing theoretical models. We will conduct several classroom experiments and related experimental data analysis to let students either identify systematic deviations (from the theories they have learned in previous undergraduate and master courses) or confirm theoretical predictions. The course will cover existing experimental methods and survey new behavioral models of individual and strategic behavior. The course is then aimed to (i) show students how economic experiments help reshape economic thinking, (ii) teach students how to set up an economic experiments clarifies the complementarities between experimental economics and behavioral economics; (iii) highlight the complementarities between experiments and econometrics (“experimetrics”). PART I: Behavioral Decision Making – G. Attanasi 1. Paradoxes of Choices under Risk 2. Elicitation of Risk Attitudes 3. Elicitation of Ambiguity Attitudes 4. Market Behavior: Call, Over-the-Counter and Double-Auctions • Classroom experiments will be performed with students as participants, and data compared to those of previous classroom experiments run with undergraduate and graduate students in the previous 15 years. PART II: Experimetrics – A. Cosma 1. Introduction to testing and Power analysis 2. Test on proportions, Binomial test, Chi-square test, Fisher exact test 3. Test on group means, parametric: t-test 4. Test on group means, nonparametric: rank tests, Wilcoxon test 5. Dependence test: Correlation, rank correlation. PART III: Voluntary Contribution Games – L. Panaccione 1. Behavioral regularities and strategies in public good provision PART IV: Other-Regarding Preferences in Social Dilemmas – S. Papa 1. Distributional Preferences 2. Belief-dependent Preferences 3. Promise keeping and Communication 4. Promise keeping and Communication: Critiques 5. Social Identity and Communication |
Research Methods in Macroeconomics
Instructors: Salvatore Nisticò, Elton Beqiraj, Carolina Serpieri
Hours: 20
Mandatory: Yes |
This course aims to present some recent developments in Dynamic Stochastic General Equilibrium (DSGE) models which are now the workhorse in macroeconomic analysis and modeling. The first part of the course outlines the characteristics of a medium-scale New Keynesian DSGE model, featuring nominal and real rigidities, endogenous capital accumulation with variable utilization rate, habit formation, investment adjustment costs, and a unionized labor market. The second part presents DSGE models with financial frictions. It shows how recent modeling developments have helped to understand the role of the financial sector in the transmission of external shocks into macroeconomic dynamics. We will focus on the role played by the financial accelerator as an amplifier of the business cycle fluctuations. This part aims to show how financial frictions can be explicitly incorporated into business cycle models. The role of credit policies in dampening cyclical fluctuations is also studied. |
Advanced Course in Innovation, Growth, and International Production
Instructors: Davide Guarascio, Michele Raitano, Jelena Reljic, Maria Enrica Virgillito, Federico Tamagni, Maurizio Franzini, Francesco Quatraro, Francesco Crespi, Anna Giunta, Enrico Marvasi, Valeria Cirillo, Luigi Marengo, Antonello Zanfei, Elena Cefis, Andrea Coveri
Hours: 20
Mandatory: No |
Digital platforms, employment, and incomes: theory and empirics - Evolutionary approaches to the economics of innovation - Innovation and employment: an economic analysis - Industry 4.0 technologies, firm performance and job flows - Firms in the GVCs: challenges in a post-covid world - Innovation and environmental sustainability - Evolutionary Economic Geography and Innovation: Theories and empirics - Robots, AI and labour markets - The engines of inequality - Wage inequality and education - Alternative perspectives on labour: knowledge and power inside organizations - The empirics of the innovation-firm growth nexus - Global Value Chains, FDIs, and Economic Performance - Innovation and firm survival. |
Technological Change and Labor Markets
Instructors: Valeria Cirillo, Dario Guarascio, Jelena Reljic
Hours: 10
Mandatory: Yes |
This course provides an overview of the theories and empirical approaches dealing with the labor market impact of technological change, focusing on automation (robots) and digitalization (digital platforms). The lectures are organized as follows. The first part of the course (three lectures) provides the theoretical foundations of the technology-employment nexus, discussing key contributions that investigate the impact that different types of innovations (e.g., product, process, and organizational innovations) can have on employment dynamics, wages, and income distribution. Both classical, neoclassical, and evolutionary approaches will be considered, emphasizing the contributions that put institutions and (heterogeneous) technological capabilities at the center of the stage. The impact of automation is investigated by reviewing recent empirical literature on the effects of robotization on employment. In this respect, a particular emphasis is placed on the data and indicators used to measure robotization and on the econometric strategies to estimate its impact on labor markets. Finally, the diffusion of digital platforms and their direct and indirect effects on labor and income distribution are discussed, providing an overview of the recent literature and major methodological challenges. The second part of the course (three lectures) is dedicated to ‘discussion classes’, in which groups of three students are asked to present one of the articles from the reading list that will be provided in advance. Presenters are expected to briefly summarize the paper’s contents, contribution and methodological approach, strengths and limitations, and potential developments |
Machine Learning in Economics
Instructors: Giuseppe Ragusa, Francesco Bloise
Hours: 20
Mandatory: Yes |
This course explores the intersection of machine learning and economics, focusing on causal inference methods. It is designed for students interested in applying machine learning techniques to economic data for robust, causal analysis. Emphasis will be placed on understanding the theory behind these methods, practical applications, and the limitations of each approach. |
Data Structure and Algorithms
Instructor: Alberto Arcagni
Hours: 20
Mandatory: No |
The course is an introduction to the programming language R. It may be useful to new users and those that started using R without knowing the basics of software development. Statistical applications guide the topics explained during the course. Main topics: Object oriented programming. Control flows and alternatives in R. Descriptive statistics as example of data manipulation. Pseudo-random number generation. Simulations. Stochastic processes. Numerical optimization, root-finding, and integration. The presentation of the igraph package. Main topics - Object oriented programming - Control flows and alternatives in R - Descriptive statistics as example of data manipulation - Pseudo-random number generation - Simulations - Stochastic processes - Numerical o Optimization o Root-finding o Integration - The igraph package for networks analysis. |
Advanced Topics in Applied Economics: Replication and Analysis of Published Research
Instructor: Leone Valerio Sciabolazza, Elton Beqiraj, Emanuele Brancati, Michele Di Maio, Giuseppe Ragusa
Hours: 20
Mandatory: No |
The course is an advanced training initiative aimed at PhD students in the program. The main focus of the course is the replication of published scientific papers in economics, an exercise designed to strengthen students' methodological, analytical, and critical skills. By replicating empirical studies, participants gain a deeper understanding of econometric techniques and identification strategies while developing practical skills in using open-source software such as R, Python, or Julia. |
PhD Workdhow in Macroeconomics
Instructor: Cristiano Cantore and Dario Bonciani Hours: 10
Mandatory: No |
The PhD in Macroeconomics Seminar Series is a student-led academic forum designed to refine doctoral candidates’ abilities to present, critique, and engage deeply with influential research in macroeconomics. Participants are assigned influential papers in the field, which they present as if they were the original authors, simulating the mastery expected in academic conferences. Another student serves as a discussant, critiquing the theoretical framework, methodology, and empirical contributions of the paper. Faculty members provide feedback on the presenter and discussant performance. This exercise helps students learn how to articulate complex ideas and engage with papers that often align with their research interests. |