| Elenco delle attività formative previste per i dottorandi del primo anno |
Mathematics
data presunta: 2024 - tipologia: riconducibile al progetto formativo - modalità di erogazione: Ex-cathedra - numero ore: 20
docente del corso: Arsen Palestini qualifica: Professore affiliazione: Italiana
programma delle attività: Topics
- 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.
modalità di accertamento finale: Esame scritto finale
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Statistics
data presunta: 2024 - tipologia: riconducibile al progetto formativo - modalità di erogazione: Ex-cathedra - numero ore: 20
docente del corso: Marco Geraci qualifica: Professore affiliazione: Italiana
programma delle attività: The aim of this course is to examine the theory of generalized linear models and their
applications, and to introduce the larger family of mixed models. Real data examples
will be provided using the R language.
1. Introduction: Statistical models and (maximum) likelihood
2. The building blocks of mean regression: The exponential family of probability
distributions
3. Generalized linear models: Theory and applications
4. Inference: Maximum likelihood for GLMs
5. Generalized linear mixed models for correlated responses
modalità di accertamento finale: Elaborato finale.
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Probability
data presunta: 2024 - tipologia: riconducibile al progetto formativo - modalità di erogazione: Ex-cathedra - numero ore: 20
docente del corso: Brunero Liseo qualifica: Professore affiliazione: Italiana
programma delle attività: An introduction to some advanced topics in probability theory. 1. Simple Random Walk
2. Markov Chains
3. Markov Chain Monte Carlo
4. Martingales
5. Brownian Motion
6. Stationary Processes
modalità di accertamento finale: Esame scritto
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Macroeconomics I
data presunta: 2024 - tipologia: riconducibile al progetto formativo - modalità di erogazione: Ex-cathedra - numero ore: 20
docente del corso: Giovanni Di Bartolomeo and Francesco Nucci qualifica: Professore affiliazione: Italiana
programma delle attività: 1) Dynamic Stochastic General Equilibrium (DSGE) models: the RBC model
We first 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 as well as on the computational aspects. In particular, 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 analyse 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 analyse 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 analyse the predictions of the model and discuss the propagation mechanisms featured in it. We also analyse 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. With regard to the positive part, it shows how the embed a NK model with elements of open economy like exchange rates, trade balance and uncovered interest parity.
modalità di accertamento finale: Esame scritto
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Microeconomics I
data presunta: 2024 (First term of first year) - tipologia: riconducibile al progetto formativo - modalità di erogazione: Ex-cathedra - numero ore: 20
docente del corso: Luca Panaccione qualifica: Professore affiliazione: Italiana
programma delle attività: Preferences. Monotonicity, strong monotonicity, local nonsatiation of preferences. Convexity of preferences.
Preferences and Utility.
The Utility Maximization problem. Walrasian demand correspondence. Indirect utility function. Marginal
utility of income. Roy's identity.
The expenditure function and its properties. The Hickian demand correspondence and its properties. The
Compensated Law of Demand.
Kuhn-Tucker conditions for the UMP and the EMP.
Derivative of the expenditure function and Hicksian demand. The Slutsky equation. Substitution and income
effect with normal and inferior goods. An example of a Giffen good.
Production set. Production Plans. Properties of the production sets. Profit maximization problem: properties
of the supply correspondence and the profit function. Cost minimization problem: conditional factor demand
correspondence, cost function, and their properties. Profit maximization problem via the cost function.
Graphical analysis of the one-output-one-input case.
Feasible allocations. Pareto efficient allocation. Walrasian (or Competitive) equilibrium for private ownership economies. Price equilibrium with transfers. Pure exchange economy. Offer curve.
The First Fundamental Theorem of Welfare Economics. The Second Fundamental Theorem of Welfare Economics.
The partial equilibrium model: individual and aggregate demand, individual and aggregate supply, equilibrium price and quantity. First and Second Welfare Theorems in the Partial Equilibrium model
modalità di accertamento finale: Esame scritto
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Econometrics
data presunta: 2024 (First term of first year) - tipologia: riconducibile al progetto formativo - modalità di erogazione: Ex-cathedra - numero ore: 20
docente del corso: Valeria Patella qualifica: Professore affiliazione: Italiana
programma delle attività: Introduction to the theory and application of cross sectional and time series econometric techniques. Emphasis is on both development of techniques and applications of econometrics to economic questions. Topics include estimation and inference in bivariate and multiple regression models, regressions with qualitative information, instrumental variables. Topics will also include the econometrics of time series, univariate and multivariate analysis, prediction and simulation, model specification and structural identification.
I. CROSS SECTIONAL ANALYSIS
• Introduction: Causal Relationships, Economic Data, Asymptotic Theory
• Linear Regression Models and Ordinary Least Squares Estimation: univariate and
multivariate regression; dummy variables; nonlinear models; OLS asymptotics.
• Endogeneity and Threats to internal validity: omitted variable bias; selection bias;
measurement error; simultaneity bias
• Instrumental variable regression: Two-Stage Least Squares
II. TIME SERIES ANALYSIS
• Autoregressive Moving Average processes
• Maximum Likelihood Estimation and Numerical Optimization
• Non-stationary ARIMA processes and Stationarity tests
• Vector Autoregressions: Vector Processes; Estimation; Model Selection and Diagnostics
• Structural VARs: Short-run Identification; Recursively Identified Models
• Prediction and Impulse Responses, Variance and Historical Decompositions, Forecasts, Counterfactuals
modalità di accertamento finale: Esame scritto
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Research Methods in Macroeconometrics
data presunta: 2024 (Second term of first year) - tipologia: riconducibile al progetto formativo - modalità di erogazione: Ex-cathedra - numero ore: 20
docente del corso: Massimiliano Tancioni qualifica: Professore affiliazione: Italiana
programma delle attività: 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) addressed.
The Monte Carlo Markov Chain (MCMC) Bayesian estimator is introduced in the
perspective of the estimation of the VAR coefficients. Identification strategies relying on theory-based sign
restrictions are then introduced as an alternative/complement of exclusion restrictions (in the latter case
resulting in mixed strategies 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.
modalità di accertamento finale: Progetto finale
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Research Methods in Microeconometrics
data presunta: 2024 (Second term of first year) - tipologia: riconducibile al progetto formativo - modalità di erogazione: Ex-cathedra - numero ore: 20
docente del corso: Marianna Belloc qualifica: Professore affiliazione: Italiana
programma delle attività: The course introduces basic tools used in microeconometrics as well as 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 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 its applications are increasing also
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
modalità di accertamento finale: Progetto finale
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Research Methods in Microeconomics
data presunta: 2024 (Second term of first year) - tipologia: riconducibile al progetto formativo - modalità di erogazione: Ex-cathedra - numero ore: 20
docente del corso: Giuseppe Attanasi, Antonio Cosma, Luca Panaccione, Stefano Papa qualifica: Professore affiliazione: Italiana
programma delle attività: Economic experiments are conducted in controlled laboratory environments in order 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 a number of classroom experiments and related experimental data analysis in order 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:
a. show students how economic experiments are useful in reshaping economic thinking;
b. teach students how to set up an economic experiment;
c. clarify the complementarities between experimental economics and behavioral economics;
d. 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
modalità di accertamento finale: Progetto finale
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Research Methods in Macroeconomics
data presunta: 2024 (Second term of first year) - tipologia: riconducibile al progetto formativo - modalità di erogazione: Ex-cathedra - numero ore: 20
docente del corso: Marco di Pietro, Elton Beqiraj e Carolina Serpieri qualifica: Ricercatore affiliazione: Italiana
programma delle attività: This course aims to present some recent developments of Dynamic Stochastic General Equilibrium
(DSGE) models, that are now the workhorse in macroeconomic analysis and modelling.
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 unionized labor market.
The second part presents DSGE models with financial frictions and shows how recent modelling
developments have helped to understand the role of the financial sector in the transmission of external
shocks into macroeconomic dynamics. In particular, we will focus on the role played by the financial
accelerator as amplifier of the business cycle fluctuations. The goal of this part is 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.
modalità di accertamento finale: Progetto finale
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Advanced Course in Innovation, Growth and International Production. Models and Data Analysis
data presunta: 2024 (Second term of first year) - tipologia: riconducibile al progetto formativo - modalità di erogazione: seminariale - numero ore: 20
docente del corso: 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 qualifica: Professore affiliazione: Italiana
programma delle attività: - 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
modalità di accertamento finale:
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ECONOMICS OF INNOVATION Technological Change and Labor Markets
data presunta: 2024 (Third term of first year) - tipologia: riconducibile al progetto formativo - modalità di erogazione: Ex-cathedra - numero ore: 20
docente del corso: Valeria Cirillo, Dario Guarascio, Jelena Reljic qualifica: Professore affiliazione: Italiana
programma delle attività: This course provides an overview of the theories and empirical approaches dealing with the labor market
impact of technological change, focusing, in particular, 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 employment effects of robotization. In this respect, a particular emphasis is
placed on the data and indicators used to measure robotization as well as on the econometric strategies
put forth to estimate its impact on labor markets. Finally, the diffusion of digital platforms and their
direct and indirect impact 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; potential
developments. The class is expected to engage in the discussion, making questions and proposing
reflections.
modalità di accertamento finale: Progetto e presentazioni
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Machine Learning in Economics
data presunta: 2024 (Third term of first year) - tipologia: approfondimenti linguistici - modalità di erogazione: Ex-cathedra - numero ore: 10
docente del corso: Giuseppe Ragusa, Francesco Bloise qualifica: Professore affiliazione: Italiana
programma delle attività: 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.
modalità di accertamento finale: Pertecipazione
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COMPUTATIONAL TOOLS FOR FINANCE
data presunta: 2024 (Third term of first year) - tipologia: riconducibile al progetto formativo - modalità di erogazione: Ex-cathedra - numero ore: 20
docente del corso: Antonio Luciano Martire qualifica: Professore affiliazione: Italiana
programma delle attività: University of Rome “La Sapienza”
Department MEMOTEF
PhD Program Models for Economic and Finance
Via del Castro Laurenziano 9, 00161 Roma (RM)
https://phd.uniroma1.it/web/MODELS-FOR-ECONOMICS-AND-FINANCE_nD3524_EN.aspx
PhD Models for Economic and Finance, 2023-2024
COMPUTATIONAL TOOLS FOR FINANCE
Teacher: Antonio Luciano Martire – RTDB - MEMOTEF
Type: Laboratory, with Google Colab
Hours: 15
Period: February-March
Syllabus
Neural networks are increasingly used to construct numerical solution
methods. They have recently also been applied to solve partial
differential equations (PDEs).
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.
Main topics
- Artificial neural networks: an introduction.
- The mathematical building blocks of neural networks.
- Types of neural networks.
- The Python Programming Language: Introduction, Keras,
TensorFlow.
- Neural networks and partial differential equations in finance.
- Financial option valuation by Artificial Neural Networks
modalità di accertamento finale: Students’ presentation of some of the course’s themes in a final seminar
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Economic Geography
data presunta: 2024 (Third term of first year) - tipologia: riconducibile al progetto formativo - modalità di erogazione: Ex-cathedra - numero ore: 10
docente del corso: F. Celata qualifica: Professore affiliazione: Italiana
programma delle attività: Seminars' descriptions and suggested readings (tbc):
Lidia Manzo | Gentrification and Diversity | 11 April 2024
This seminar introduces the limits, ambiguities, and power of resistance to gentrification and anti-displacement practices in the multi-ethnic community of Milan Chinatown in the Paolo Sarpi street neighborhood. I elaborate on this debate by examining the specific role of urban diversity in redefining inclusion and exclusion in contemporary cities experiencing urban revitalization. We will discuss two concepts: the changes brought to the neighborhood by an influx of more affluent residents and businesses (“gentrification”) and the sociocultural diversity lens (“Chinatown and Chineseness”). In doing so, we will problematize the debate around spaces of encounter by pushing for a more nuanced understanding of the link between the dynamics of negotiation, co-production, and resistance to the rebranding of multiethnic areas of the city. This seminar will also focus on the ambiguous relationship between diversity and inclusion/exclusion to question the assumption that immigration and gentrification prevent each other. The case of Milan’s Chinatown clearly shows that the two phenomena can work in concert.
Keywords: gentrification, urban diversity, resistance, Chinatown, Milan.
Readings:
- Manzo, L.K.C. (2023) Gentrification and Diversity: Re-branding Milan’s Chinatown, Cham: Springer.
- Manzo, L.K.C. (2016) «Via via, vieni via di qui!» Il processo di gentrificazione di via Paolo Sarpi, la Chinatown di Milano (1980-2015) (in Italian) [The process of gentrification of Paolo Sarpi street, the Milan’s Chinatown 1980-2015], Archivio di Studi Urbani e Regionali, 117, 27-50.
Sarah Marie Hall | Seeking solidarity, finding friendship: co-creation as method and praxis for feminist economic geographers | 3 June 2024
Co-production and co-creation are activities that span feminist activism and practice, including and beyond academic spaces. This can include in writing, events and outputs, and is often framed by the mantra ‘the personal is political’. This talk focuses on co-creation as a method and praxis for feminist economic geographers, and draws on my experience of working in collaboration over a long period with Inspire, a women’s wellbeing organisation based in Oldham, UK. With our work together we have explored women’s economic empowerment, austerity and altered lives, and creative methodological innovation. With this contribution I will share personal reflections about working alongside, investing in and staying with community groups. I will present examples of how this relationship evolved, from working together within a broader consortium, to experimenting together, to going it alone. This includes practical tips on navigating roles of academic, advisor, expert, and friend. I close by sharing our ongoing plans of co-investment between Inspire and my team, and how collaborative future-making can provide important spaces of hope and solidarity in the research community.
Filippo Celata | Anglophone and non-anglophone critical geographical traditions: Geografia Democratica (1976-1981)
Geografia Democratica is a collective of scholars that during the second half of the 1970s sought to promote a critical and radical turn in Italian academic geography. The seminar will provide a critical reading of the controversial history of the collective and of some of its components, in light of ongoing debates upon the pluriversal roots of contemporary critical geographies and the role of ‘other geographical traditions’ - beyond the Anglo-American hegemony. The aim is, in particular, to discuss Geografia Democratica as a ‘rupture experience’ in the mainstream of Italian geography, how it intersected or not similar turns occurred in the Anglo-American geographies of the time, and what is its legacy for today’s critical geographers in Italy and beyond.
Readings:
- Celata F. & Governa G. (2023) Reclaiming other geographical traditions: The hidden roots of Italian radical geography. Transactions of the Institute of British Geographers.
- Extracts from key texts by Geografia Democratica and some of its members. .
modalità di accertamento finale: Discussion and presentation
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COMPUTATIONAL TOOLS FOR STATISTICS
data presunta: 2024 (Second Term) - tipologia: riconducibile al progetto formativo - modalità di erogazione: Ex-cathedra - numero ore: 15
docente del corso: Alberto Arcagni qualifica: Professore affiliazione: Italiana
programma delle attività: The course is an introduction to the programming language R. It may be useful
both to new users and to ones 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.
modalità di accertamento finale: Submission of assignments by the students about the topics covered during the course
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