These lectures aim at revisiting the standard approach to several strategic settings in which players’ information is imperfect and/or incomplete. We will first present the relevant solu- tion concepts for extensive form games, and then analyze the scenario in which players may have some commitment power. This allows to cover important issues in incentive theory and mechanism design. Our emphasis will be on economic applications. No background beyond an introductory course in game theory is required.

This course will be held at EIEF.

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Behavioral economics observes regularities in individual decision-making, documents departures from what classical economic theory predicts, and explains these departures by incorporating psychological insights into economic theories. This course covers recent topics in behavioral economics. The first part includes the analysis of deviations from the standard neoclassical model due to imperfect optimization (i.e., limited attention, limited information, limited computational capacity), bounded self-control (i.e., reference dependence, framing, status quo bias), nonstandard preferences (i.e., social preferences, fairness). In the second part, the course gives some applications of behaviorally motivated policy interventions in specific fields, mostly environmental economics but also public economics, household finance, and others.

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One of the central applications of economics is the evaluation of policies and interventions. Causality is important in econometrics because it enables economists to go beyond mere correlations, providing a deeper understanding of how economic variables interact and influence each other. This understanding is essential for making sound economic decisions, designing effective policies, and advancing economic theory. For example, it helps determine whether an increase in the minimum wage directly leads to changes in employment levels. This course delves into advanced topics in causal inference, particularly focusing on 'irregular designs.' Irregular designs represent complex scenarios where the standard assumptions required for estimating causal effects do not apply. Examples of irregular designs include randomized experiments affected by non-compliance, as well as observational studies with unmeasured confounding variables. Additionally, we explore other challenging irregular designs, such as regression discontinuity designs, where treatment assignment relies on specific thresholds, leading to issues of overlap. In the course's second segment, our exploration extends to the realm of spillover effects. Here, we transcend the common assumption of independence between units and investigate how the treatment of one unit may reverberate onto the outcomes of others. We introduce cutting-edge statistical methodologies tailored for estimating spillover or peer-influence effects, particularly within clusters of units or social networks.

This course is shared with the Master of Science in Economics.

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The aim of the course is to introduce the students to the microeconomic analysis of pollution policy. A base model will specifically be presented to address pollution generation and environmental policy design. This model will then be extended in several directions, including (but not limited to) eco-innovation drivers and transboundary/international pollution problems (the latter also featuring a macroeconomic perspective). For the covered topics, a general theoretical analysis will be developed, and then examples of current theoretical and empirical research will be provided.

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Part 1, Gianluca Cubadda

1) Dynamic regression models with many predictors
2) Out-of-sample evaluation
3) Classical methods for large vector autoregressive models (VARs)

Part 2, Stefano Grassi

The objective of this module is to introduce students to advanced topics in macroeconometrics to enhance independent research. Examples of active topic of research will be provided during the lectures.

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This course is centered on a growing literature which extends the traditional theories of incentives and optimal contracting to a competitive framework. Our focus is on strategic approaches. We first provide a methodological introduction, which aims at revisiting the foundations of mechanism design and its recent extensions to competing mechanism games. We next move to competitive settings, covering both moral hazard and adverse selection economies. Hopefully, the lectures will provide new insights to evaluate the effects of financial con- straints due to the combination of agency problems and competition among intermediaries on economic activity and market performances. No background beyond first year graduate microeconomics is required, although familiarity with contract theory and information economics is useful.

This course will be held at EIEF.

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Nel 2024 Carlo Ciccarelli curerà su questo tema un numero speciale della rivista  EREH  https://academic.oup.com/ereh  insieme al collega Dan Bogart (https://sites.socsci.uci.edu/~dbogart/)

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The course deals with methods and models for signal extraction and filtering in economics.
It sets off with an introduction to time series analysis in the frequency domain. Frequency domain methods focus on the spectral density function of stationary stochastic process. Inferences are based on the periodogram, which is a transformation of the series, based on the discrete Fourier transform, with interesting properties that bring out features of the series and facilitate inference for classes of time series models. The asymptotic properties of the periodogram will be considered and used for estimating the spectrum of a random process.
For a given time series model, parametric inferences can be based on a large sample approximation to the true likelihood known as the Whittle likelihood. Our illustrations deal with estimation of ARMA, unobserved component models for trend-cycle analysis, parametric and semiparametric long memory models for stochastic volatility, and high-dimensional factor models.
High-dimensional factor models is an approach to high-dimensional time series that plays a pivotal role for signal extraction and forecasting. They are based on a solid representation theory and provide the way of distilling the co-movements in a large set of macroeconomic time series, without incurring in the curse of dimensionality.
We conclude with an introduction to the class of locally stationary processes.

Major references:
Brockwell, P.J. and Davis, R.A. (1991), Time Series: Theory and Methods, Springer-Verlag, New York, Chapters 4 and 10
Dzhaparidze, K., (1986), Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series
Hamilton, J. D. (2018). Why you should never use the Hodrick-Prescott filter. Review of Economics and Statistics, 100(5), 831–843

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Instructor webpage: Proietti Tommaso

This course focuses on New Keynesian model with frictions in the real and financial sectors and on the role of fiscal and monetary policies in mitigating business and financial cycle fluctuations. We will consider among others the role of recent unconventional monetary and macroprudential policies as business cycle stabilization devices and monetary and fiscal policy coordination during the Pandemic. As modern economies are increasingly exposed to shocks of large magnitude, the course will also focus on the role of macroeconomic policies in DSGE models subject to rare disaster shocks.
We will consider Dynamic Stochastic General Equilibrium (DSGE) models where consumers, firms, banks and the public sector (monetary and fiscal policy) interact in the same economic environment and produce choices in terms of consumption, investment, output and monetary aggregates.
Macro models of monetary policy in a DSGE setting typically involve forward-looking behavior. To develop practical research skills students will solve linear rational expectation models using MATLAB.
Focussing in particular on the method proposed by King and Watson (1998) we will then solve a larger DSGE model with a banking sector producing the full constellation of financial and monetary spreads as proposed by Goodfriend and McCallum (2007). We consider the macroeconomic effects of unconventional monetary policy, through variation in the composition of central banks.balance sheets, as compared to the conventional monetary transmission mechanism.

Major references:
Gali, J. (2008). Monetary Policy, Inflation and the Business Cycle: An Introduction to the New Keynesian Framework, Princeton University Press
Corrado, L., Grassi, S. and Paolillo, A. (2023). Identifying Large Economic Shocks in a Disaster Environment, mimeo
Goodfriend M. and B.T. McCallum (2007). Banking and interest rates in monetary policy analysis: A quantitative exploration, Journal of Monetary Economics, 54, 1480-1507
Blanchard, O. and K. C. Kahn (1980), The Solution of Linear Difference Models under Rational Expectations, Econometrica, 48(5), 1305-11
King, R.G and M.W. Watson (1998), The solution of singular linear difference systems under rational expectations, International Economic Review, 39 (4)

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Instructor webpage: Corrado Luisa

The course introduces some key topics in Development Economics. It first discusses models with multiple equilibria, where a discrete development effort (a big push) may be required to move the economy away from a low-income, low-growth trap. It then examines the dual structure of labour markets in developing countries and explains migration as a response to the imbalance between different sectors or regions. We shall consider non-ergodic development models, where the long-run growth path of the economy depends on the initial distribution of income and wealth. We shall finally study whether weak states can act as a barrier to economic development.

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Instructor webpage: Scaramozzino Pasquale

Industrial organisation is the branch of economics that studies the role of imperfectly competitive markets for private and social decisions. The course aims to introduce the students to its fundamental models and results. The course is designed for graduate students who either did not take a course in IO or only took a basic undergraduate IO course. It serves either as a general interest course for those students not wanting to pursue research in IO or as an introduction to more advanced IO courses for those who want to do research in IO.

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The course will briefly introduce latent variable models for empirical exercises in economics, finance, and business. We will give an overview of possible specifications of univariate and multivariate latent variable models, with special attention to interpretation and connections with the concept of unobserved heterogeneity. We will mostly focus on latent Markov models with categorical outcomes, but most of the concepts apply in general. The R software for statistical computing will be used throughout.

Major references:
Bartolucci, F., Farcomeni, A. and Pennoni, F. (2013), Latent Markov Models for Longitudinal Data, Chapman & Hall/CRC Press

This course will be held at EIEF.

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Instructor webpage: Farcomeni Alessio

1. Credit: Trust and limited commitment; Intermediating trust: the role of banks; Financial fragility.
2. Money: Commodities as money; Assets as money; The terms of trade; Intermediation.
3. Money: The benchmark model with divisible money; Extensions; The cost of inflation; Liquidity in finance.

Major references:
A list of papers in the syllabus

This course will be held at EIEF.

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Instructor webpage: Mattesini Fabrizio

This course is an introduction to the study of distributional issues in history and today. History offers a vast catalogue of experience from which to learn: radical technological changes, wars, internationalisation and autarchy, economic miracles and development disasters, and every type of policy regime. And only a historical perspective puts us in a position to identify the operation of long-run laws (or, conversely, the role of contingency) in an economy’s evolution. Equally important in understanding and interpreting the literature on poverty, inequality, and living standards is a sure grasp of the technical issues surrounding measurement. Establishing a poverty line that is comparable across very different circumstances is a theoretical and practical challenge, as is estimating a distribution from microdata (today no less than in the past). Alongside the history of inequality, therefore, the course includes an important component devoted to the econometric theory and practice of measurement.

Major references:
Allen, R. “Absolute Poverty: When Necessity Replaces Desire,” American Economic Review, vol. 107 no. 12 (2017), pp. 3690-3721
Lindert, P. and Williamson, J. Unequal Gains: American Inequality since 1700. Princeton: Princeton University Press, 2016
Milanovic, B, P. Lindert and J. Williamson, “Pre-industrial inequality,” Economic Journal, vol. 121 (2011), pp. 255-272
Piketty, T. Capital in the Twenty-First Century. Cambridge, Mass: Harvard University Press, 2014
Ravallion, M. 2016. The Economics of Poverty. History, Measurement and Policy. New York and Oxford: Oxford University Press
Vecchi, G. Measuring Wellbeing. A History of Italian Living Standards. New York: Oxford University Press, 2017

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Instructors webpages: Brian A'Hearn, Vecchi Giovanni

The aim of this course is to introduce students to a set of tools for modeling and prediction with complex (long and wide) datasets. This is a recently developed area in statistics and econometrics which blends with parallel developments in computer science, in particular machine learning. The course encompasses a variety of methods, both frequentist and Bayesian, including classical meth- ods for regression and classification; asymptotic approximations vs. resampling methods; model uncertainty and model selection; model averaging; shrinkage estimators; principal components and partial least squares; linear regression smoothers; projection pursuit, generalized additive models, and neural networks; tree-based methods.

Major references:
Hastie T., Tibshirani R., and Friedman J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer: New York
James G., Witten D., Hastie T., and Tibshirani R. (2013). An Introduction to Statisti- cal Learning with Applications in R. Springer: New York

This course will be held at EIEF.

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Instructor webpage

The macro reading group offers a forum to read, present, and discuss current research in macroeconomics. In each meeting, one of the students presents a paper of their choice, followed by a general discussion. Students from all cohorts are welcome to attend, and more senior students (second-year and above) are encouraged to present their own research projects. By attending the macro reading group, students familiarize themselves with the macro literature, learn how to present and provide constructive feedback, and get inspiration for their own research.

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The reading group in Quantitative Finance and Financial Econometrics focuses on the intersection of financial econometrics, mathematical finance,  and the theory and practice of stochastic processes applied to finance and insurance problems, including risk management. In each session, a member presents recent research or a significant paper, sparking discussions that aim to familiarize students with the state-of-the-art in these sectors. This initiative encourages participants to engage with current methodologies, critique approaches, and explore innovative solutions in a collaborative setting. Open to all expertise levels, the group's ultimate goal is to enhance understanding and application of quantitative finance among students, preparing them for advanced research or professional challenges in these fields.

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We plan to discuss published and recent working papers in Microeconometrics and Causal Inference, with a focus on methodological issues and technical challenges often encountered in the field. Ongoing research within our department is also welcome. Our aim is to advance our understanding of current microeconometric methodologies, all within a collaborative and informal setting.

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Discussion on papers recently published in leading economic journals or recent working papers by leading scholars in the field of political economy or labor economics (broadly defined). Papers looking at related topics in historical contexts/using historical data are also welcome.

Logistic organizer: Francesco Barilari

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