Second level Master in Economics Academic year 2015/2016

Director: Professor Tommaso Proietti

The Master in Economics program is designed to provide the analytical and quantitative skills that

are needed for a successful career as a professional economist in business or government. The

program is also designed to provide intensive training to students who want to pursue a Ph.D. in

Economics, Econometrics or Finance in a leading research university.

The Master in Economics has an excellent track-record in placing students in both academic and

non-academic institutions. In the last three years, about 80% of our students have been successfully

enrolled in Ph.D. Programs in top U.S. and European Universities, while 20% have found jobs in

non-academic institutions in Italy or abroad.

Program objectives and qualification profile

After an intensive training during the academic year, students will be well-prepared as economists

to start a career in:

- Research offices of national and international governmental economic institutions

- Large corporations

- Large banks

- Financial institutions and research centres

FALL TERM (Sep. 14 - Dec 18, 2015) Course Instructor Credits Period

CO

MP

ULS

OR

Y U

NIT

S

Review Courses_Mathematics

Calculus Gibilisco 0

Sept. 14 - Oct 30 Linear Algebra Gibilisco 0

Optimization Gibilisco 0

Probability Gibilisco 0

Core Courses_Econometrics Static Regression Peracchi 3 Nov. 9-27

Instrumental Variables and GMM Peracchi 3 Dec. 4 -18

Core Courses_Microeconomics

Consumption and Production Theory Iozzi 3 Nov. 9-27

General Equilibrium Iozzi 3 Nov. 30 - Dec. 18

Core Courses_Statistical Computing 1

Introduction to Stata, part 1 Gagliarducci 1 Nov. 10 - Dec. 15

Introduction to Matlab, part 1 Parisi 2 Sept. 16 - Nov. 6

Programming in Matlab Ramponi 3 Sept. 17 - Dec. 3

Core Courses_Statistics Statistics Mezzetti 3 Oct. 19 - Nov. 13

21 CFU

Elective Units

Finance Asset Pricing Herzel 3 Sept. 21 - Oct. 9

Statistical Computing 1 Data Mining and Statistical Learning Proietti 6 Sept. 28 - Oct. 30

9 CFU

SPRING TERM (Feb. 15 - May 6, 2016)

CO

MP

ULS

OR

Y U

NIT

S

Macroeconomics Consumption and Investment Waldmann 3 Feb. 15 - Mar. 4

Growth theory Waldmann 3 Mar. 7 - 24

Macroeconometrics Univariate Time Series Cubadda 3 Feb. 15 - Mar. 4

Bayesian Time Series Econometrics Korobilis (U. Glasgow) 3

Statistical Computing 2 Introduction to Stata, part 2 Gagliarducci 2 Feb. 15 - Mar. 24

Introduction to Matlab, part 2 Parisi 1 Feb. 15 - Mar. 4

Applied Econometrics with Stata Belotti 3

18 CFU

ELEC

TIV

E U

NIT

S

Finance Advanced Topics in Finance tba 3

Credit Risk Models Renò (U. Siena) 3 Feb. 15 - Mar. 4

Financial Market Models

Robotti (Imperial College London)

3 Mar. 4 - May 13

Theory of Banking Campioni 3 Feb. 15 - Mar. 4

Financial Econometrics Forecasting Financial Time Series Brunetti 3 Apr. 26- May 13

Volatility Modelling and Forecasting Proietti 3 Apr. 4 - 22

PROGRAMME STRUCTURE

Labour Market Labour and Personnel Economics_Part 1 Gagliarducci 3 Feb. 21 - Mar. 11

Labour and Personnel Economics_Part 2 Vuri 3 Mar. 14 - 30

Topics in Labour Economics Danziger (Ben-Gurion U.) 3 Feb.15 - Mar. 4

Macroeconomics Real Business Cycles Annicchiarico 3

Heterogeneous Agents Ferraris 3

Business Cycle and Economic Policy Corrado 6

Topics in Monetary Economics

Araujo (Michigan State University)/Mattesini

3

Macroeconometrics Advanced Time Series Proietti 3

Computational Macroeconomics Corrado 2 May 16 - 27

High Dimensional Covariance Estimation

Pourahmadi (Texas A&M U.)

May

Macroeconomic Forecasting Espasa (U. Carlos III) 3

Multivariate Time Series Cubadda 3 Mar. 7 -24

Microeconomics 2 Advanced Topics in Economics tba 3

Theory of Incentives Attar 3 April - May

Information Economics, Game Theory and Auctions

Valletti 6 Apr. 4 - May 13

Microeconometrics How to write in International Journals De Fraja 1

Microeconometrics using Stata Weeks (U Cambridge) 3 April 6-8?

Public Economics International Economics Corrado 3 March 7 - 23

Environmental Economics Zoli 3 Feb. 16 - Mar. 4

Topics in Economics of the Environment D'Amato 3

Topics on Procurement of Public Services Iossa-Spagnolo 3

Welfare, Inequality and Poverty Measurement

Vecchi 3

15 CFU

Final thesis

3 CFU

Total

60 CFU

COURSE PROGRAMS

FALL TERM - COMPULSORY UNITS

MATHEMATICS – Paolo Gibilisco

0 credits

Linear Algebra

Systems of linear equations. Matrix Algebra. Algebra of square matrices. Transpose and its

properties. Determinant. Groups, fields, vector spaces. Linear independence and basis. Dimension

of vector spaces. Linear transformations. Kernels. Scalar products. Cauchy-Schwartz inequality.

Eigenvalues, eigenvectors and the characteristic polynomial of a square matrix. Basic properties of

eigenspaces. Symmetric, symmetric and orthogonal matrices. Positive definite matrices. Projection

operators. Cholesky decomposition. Diagonalizable matrices. The spectral theorem.

Calculus

Series. Power series. The complex numbers. Complex power series and complex exponential. The

Euler formula. Differentiability for functions of several variables: examples and counterexamples.

The gradient. The Jacobian matrix. The chain rule for differentials. Mixed partial derivative. The

Schwartz (Young) theorem. Integration in n dimension. The Fubini theorem. The change of variable

formula. Integration using polar coordinates. Differentiation under the integral sign. Introduction to

differential equations. The Cauchy problem. The L2 scalar product on R2, on C[0,1] and for

random variables.

Optimization

The Taylor polynomial in n-dimensions. The Hessian matrix. Unconstrained optimization:

necessary and sufficient conditions for maxima and minima. Constrained optimization. Lagrangian

function and Lagrange multiplier. Introduction to Kuhn-Tucker.

Probability

Elements of a probability space. Algebras of events and information about random experiments.

Introduction to combinatorial calculus. Finite probability spaces, probability measures, introduction

to Kolmogorov theory. Conditional probability, total probability formula, Bayes formula.

Independent events. Random variables and their properties. Probability distribution, distribution

function and densities function of a random variable. Expectation and variance of a random variable

and their properties.

Expectation and variance for the main kinds of random variables. Covariance and scale-invariance

of the correlation coefficient. Random vectors and their properties. Probability distribution,

distribution functions and densities functions of a random vector. Independent random variables,

covariance and correlation. Conditional expectation of a random variable and its properties.

Conditional expectation as best estimator. Geometric approach to the conditional expectation.

Sequences of random variables. Convergence in probability and in law. The (weak) law of large

numbers. The characteristic function. Central limit theorem. Multivariate Gaussian distribution.

Conditional expectation for the bivariate gaussian.

STATISTICAL COMPUTING

Pass or Fail Exams

Introduction to Matlab – Antonio Parisi - 3 credits

The course provides an introduction to Matlab (basic commands, control flow statements, m-files)

under a statistical perspective. Econometric methods will be applied using real-world datasets for

both univariate and multivariate time series. The course will cover different models (ARMA, VAR,

EC models) and related statistical tests. Students should be able to implement the methods and

interpret the results.

Programming in Matlab – Alessandro Ramponi – 3 credits

Matlab environment: Variables and constants, operators and simple calculations, formulas and

functions. Matrices and operations with matrices. Relations and Booleans. Working with data:

reading and writing, file handling, preprocessing data, summarizing data, visualizing data. MatLab

graphic functions.

Programming in Matlab: Algorithms and structures, control flow (conditional control, loop control,

vectorization, preallocation), MatLab scripts and functions (subfunctions, nested functions and

function handles).

Applications and examples: Introduction to numerical methods (linear systems, iterative root-

finding methods) and symbolic calculus.

Introduction to Stata – Stefano Gagliarducci – 3 credits

The aim of this course is to acquaint students with the basics of Stata, and its use for applied

economics. The course will be mostly focused on microeconometrics. Topics to be covered include:

dataset management, descriptive statistics, graphics, loops, linear regression, instrumental variable

models (IV). A few classes will also be devoted to the introduction to panel data models, difference-

in-difference models (DiD), dynamic panel models, regression discontinuity (RD).

STATISTICS – Maura Mezzetti

6 credits

Principles of Data Reduction: The Sufficiency Principle (Exponential family, Sufficient), Ancillary

and Complete Statistics; Minimal Sufficient Statistics, the Likelihood Principle (the Likelihood

Function). Point Estimation: Methods of Finding Estimators: Methods of Moments, Maximum

Likelihood Estimators, the EM Algorithm, Methods of Evaluating Estimators (Mean Squared Error,

Uniform Minimum Variance), Estimators, Fisher Information, Loss Function. Neyman-Pearson

Lemma. Confidence Intervals. Hypothesis Testing: Methods of Finding Tests (Likelihood Ratio

Tests, Score Test, Wald Test), Methods of Evaluating Tests (the Power Function), Powerful Tests,

Loss Function. The p-value. Notes on Bayesian Inference. Non Parametric Inference: Kolmogorov-

Smirnov Test. Tutorials. Exercices.

ECONOMETRICS – Franco Peracchi

6 credits

Static regression

Introduction and review: Conditional means and conditional variances. Potential outcomes and

causal effects. Models for conditional means and conditional variances. Best linear predictors. The

classical linear model and the OLS estimator: Elements of a linear model. The OLS estimator.

Algebraic properties. Sampling properties of OLS. Exact sampling properties under ideal

conditions. Misspecification of the regression function. Misspecification of the variance function.

Asymptotic properties. Estimates of statistical precision. Inconsistency of OLS. GLS and feasible

GLS: The GLS estimator. Feasible GLS. Asymptotic properties. Diagnostic procedures: OLS

residuals; Transformations of OLS residuals; Recursive residuals; Influence and leverage.

Hypothesis testing and model selection: The classical t and F tests; Asymptotic properties of

classical tests; Likelihood-based tests; Specification tests; Model selection criteria.

Instrumental Variables and GMM

The instrumental variables (IV) method: Moment conditions; The method of moment (MM) or

simple IV estimator; The class of IV estimators; Sampling properties; Estimates of statistical

precision; Hypothesis testing; 2SLS. Estimation of causal effects; The fundamental problem of

causal inference; Approaches to estimation of average treatment effects; Estimating the returns to

education. 2SLS under weak instruments: Motivating examples; Definitions and basic models; The

bias of 2SLS; Standard asymptotic approximations; Alternative asymptotic approximations;

Detecting weak instruments. Robust inference under weak instruments: Hypothesis testing;

Confidence sets; k-class estimators; Bias-corrected estimators; Other approaches; Practical

recommendations. The generalized method of moments (GMM); Moment restrictions; MM

estimators; GMM estimators; Asymptotic properties. Weak identification and robust inference in

GMM; Definitions; Asymptotic approximations; Detecting weak identification; Robust inference;

Robust estimators.

MICROECONOMICS - Alberto Iozzi

6 credits

The primary purpose of this course is to illustrate the microeconomic theory examining the

behaviour of the most important sets of economic agents – the individual (household) and the firm-,

and the functioning of competitive markets. The material covered in this course is important in its

own right, as a description and explanation of economic agents acting in rational manner, but also

as the foundation for macroeconomics and for the many specialist subjects within economics.

The course consists of a combination of lectures and revision classes. The majority of the formal

material will be presented in the lectures: the revision classes are mainly devoted to technical

exercises and as such are a crucial ingredient of learning to do microeconomics yourself.

• Consumption: preferences and utility, consumer’s problem, indirect utility and expenditure,

consumer demand

• Production: technology, profit maximization, cost minimization, competitive firm • Choice under

uncertainty: expected utility

• General equilibrium: existence, efficiency, contingent plans

FALL TERM - ELECTIVE UNITS

ASSET PRICING – Stefano Herzel

3 credits

Our main topic is financial derivatives. We will study the pricing by arbitrage approach under

different settings. We will consider different derivatives and different models. We will use Matlab

to implement the models. The Binomial Model (Cox-Ross-Rubinstein). Wiener Processes and Ito’s

Lemma. The Black-Scholes-Merton Model. Options on Stock Indexes. Options on Currencies.

Options on Futures.

DATA MINING and STATISTICAL LEARNING -Tommaso Proietti

6 credits

Introduction to data mining. Tools for data analysis, visualisation and description.The linear

regression model. Model selection and evaluation: bias-variance trade-off, model complexity and

goodness of t. Cross-validation. Selection using information criteria. Regularization and shrinkage

methods: rigde regression, lasso, forward stagewise regression. Principal components regression.

Linear methods for classication: Bayes Classication Rule. Discriminant analysis. Canonical

variates.Logistic regression. Semiparametric regression: Regression splines and smoothing splines.

Kernel smoothing methods: Local polynomial regression. Density estimation. Nearest neighbor

classication. Additive Models, tree-based methods. GAM, Regression andclassication trees.

Boosting.

SPRING TERM – COMPULSORY UNITS

MACROECONOMICS - Robert Waldmann

6 credits

Growth theory

Solow Model Review. The Ramsey Cass Koopmans Model I. Ramsey Cass Koopmans Model II.

The Romer 86 model. The Romer 90 model I. The Romer 90 model II. Human Capital and Growth.

Consumption and Investment

Stochastic implications of the Permanent Income Hypothesis. The overlapping generations model

with money. Fixed Capital Investment. Inventory investment. Credit Rationing.

MACROECONOMETRICS

6 credits

Univariate Time Series – Gianluca Cubadda

Stationary time series analysis: Basic concepts. Stationarity, autocorrelation, partial autocorrelation.

Linear stationary processes. ARMA models. Forecasting. Nonstationary time series analysis:

ARIMA models. Seasonality, The Box-Jenkins approach. Unit roots in macroeconomic time series:

Deterministic trends vs. random walks. Unit-roots tests. The Beveridge-Nelson trend-cycle

decomposition. Impulse response function and measures of persistence.

Bayesian Time Series Econometrics – Dimitris Korobilis (U. Glasgow)

Bayesian inference for some simple statistical models. Choice of initial distribution. Bayesian

procedures. Bayes factor. Computational methods. Montecarlo, importance sampling, Montecarlo

Markov Chain (MCMC). Linear models.

STATISTICAL COMPUTING

Pass or Fail Exams

Applied Econometrics with Stata – Federico Belotti - 3 credits

IV-GMM: Jacknife, k-class estimators, Testing for Weak Instruments, Second stage robust to weak

instruments inference procedure: Conditional Likelihood ratio tests, Moreira tests, Anderson Rubin.

Basic Stata Programming and Graphs: intro to MATA, Monte Carlo Simulations, Maximum

likelihood. LPM, Logit, Probit, Multinomial models with SHIW data. Panel data, Standard panel

models. Programming FE, RE, in MATA. Dynamic Panel Data: Theory and empirical applications.

Weak instruments in dynamic panel data. Bias corrections looking at the Arellano-Bond and

Blundell-Bond class estimators. Latex and STATA.

SPRING TERM - ELECTIVE UNITS

FINANCE

Advanced Topics in Finance - tba - 3 credits

This course is devoted to the presentation of the most advanced techniques in finance presenting a

selection of the most interesting and stimulating results. The choice of the topics and the structure

of the course will depend on the instructor who will typically be a visiting scholar.

Credit Risk Models – Roberto Renò (U. Siena) - 3 credits

Sovereign debt: Stochastic Calculus for Credit Risk Models (review); Poisson Processes; Modeling

the default intensity; Sovereign Credit Risk; Bond and CDS pricing.

Financial Market Models – Cesare Robotti (Imperial College London) - 3 credits

Introduction. Financial Markets Definitions and Financial Securities. Efficient Portfolios and

Efficient Frontier. Correlation Structure Security Returns: Single and Multi Factor Model. Capital

Assets Pricing Model. Efficient Markets Hypothesis. The course's objectives are the following:

Teach the student the tools used in financial markets to evaluate the stock return and firm financial

performance; Develop the analytical skills and mindset necessary to make decisions about how

market react to an expected/unexpected new information; Instruct how to value firms' assets;

Develop concise writing and oral presentation skills relative to cases and term projects; Have a

working knowledge of MatLab/STATA to apply empirically the concepts of financial markets.

Theory of Banking – Eloisa Campioni - 3 credits

Introduction to time, uncertainty and liquidity. Microeconomic foundations for financial

intermediation. Why do banks exist. The effects of banks on financial markets: credit rationing;

transmission mechanisms from the financial to the real sector. Bank runs and remedies to

instability. An analysis of the interbank market.

FINANCIAL ECONOMETRICS

Forecasting Financial Time Series – Marianna Brunetti - 3 credits

Mixed Data Sampling (MIDAS): Introduction, estimation, properties, examples. Forecasting with

mixed (and high) frequency data. Forecast accuracy. Introduction: schemes, number of

observations, why out of sample, measures of accuracy (MSFE, MAFE, forecast encompassing).

Comparing small number of models: nested and non-nested models Comparing large number of

models. Applications: forecasts of Business Cycle, Exchange Rates, Interest rates. Econometrics for

option prices.

Volatility Modelling and Forecasting – Tommaso Proietti - 3 credits

Introduction: Asset returns. Stylized facts: asymmetry, kurtosis and volatility clustering. Stochastic

processes: stationarity, purely random processes (white noise). Random walks and martingales.

Review of prediction theory. Optimal prediction. Forecasting with non-stationary models:

exponential smoothing. Volatility measurement and analysis: Autoregressive Conditional

Heteroschedasticity (ARCH): model specification, properties, maximum likelihood estimation,

prediction. Extensions: ARCH in mean. Generalized ARCH models, Integrated GARCH,

Exponential GARCH models. Multivariate GARCH models. VEC and BEKK. Conditional

correlation models: CCC, DCC. Factor models: Factor GARCH, O-GARCH 2.4 Realized volatility.

Risk measurement: Value at Risk and expected shortfall.

LABOUR MARKET

Labour and Personnel Economics, part 1 – Stefano Gagliarducci - 3 credits

Labor supply (retirement, family), labor demand, labor market equilibrium (minimum wages,

payroll taxes, immigration, wage distribution).

Labour and Personnel Economics, part 2 – Daniela Vuri - 3 credits

Education (human capital, signaling, school quality), discrimination (race, gender), probation, pay

based on performance (piece-rate, team).

Topics in Labour Economics – Lief Danziger (U. Bengurion) - 3 credits

Extension of labor contracts and optimal backpay. Endogenous monopsony and the perverse effect

of the minimum wage in small firms. Uniform and nonuniform staggering of wage contracts.

Noncompliance and the effects of the minimum wage on hours and welfare in competitive labor

markets.

MACROECONOMICS

Real Business Cycles – Barbara Annicchiaro - 3 credits

The aim of this course is to achieve three objectives:

- provide an introduction to the so-called New Neoclassical Synthesis for the business cycle

analysis, starting from basic RBC and NK models;

- familiarize students with the state-of-the art macroeconomic modelling techniques;

- provide a hands-on introduction to simulation of macroeconomic models using Dynare, a software

platform for handling DSGE models.

Heterogeneous Agents – Leo Ferraris - 3 credits

This course aims to introduce students to heterogeneous agents models, in the Arrow-Debreu

tradition, with complete and incomplete markets, but also in a static and a dynamic setting and with

finite time horizon. In the second part, the course will focus on endogenously incomplete markets

models (of the Kehoe-Levine type).

Business Cycle and Economic Policy – Luisa Corrado – 6 credits

This course aims at developing practical research skills for macroeconomists. In particular we will

consider Real Business Cycle Models, New Keynesian model with frictions in the real and financial

sectors and the role of fiscal and monetary policies. We will consider among others the role of

recent unconventional monetary and macro prudential policies as business cycle stabilization

devices. 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.

Topics in Monetary Economics – L. Araujo (Getulio Vargas Foundation, Sao Paulo), Fabrizio

Mattesini - 3 credits

The aim of the course is to introduce the students to the theory of money both as a store of value

and a medium of exchange. Two workhorse models of money will be discussed: i) the overlapping

generations (OLG) model where money is a store of value and ii) the search and matching model

(Aka Kiyotaki/Wright model) where money is a medium of exchange. The course is geared towards

post-graduate students with some analytical training.

MACROECONOMETRICS

Advanced Time Series – Tommaso Proietti – 3 credits

Unobserved components models for economic time series. Models for the trend component.

Cyclical components. Seasonality and Calendar components. Outliers and structural breaks. State

space models and their statistical treatment. Dynamic factor models. Kalman Filter. Maximum

likelihood estimation Smoothing filters Forecasting, Diagnostics. Regime Switching Models.

Matlab tutorials.

Computational Macroeconomics – Luisa Corrado – 2 credits

This course aims at developing practical research skills for macroeconomists. In particular we will

consider stylised 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 behaviour and

traditional techniques, such as Blanchard and Kahn's (1980) method, are subjected to several

limitations. In particular, in order to define the solution, we need as many state variable as there are

stable roots. Several numerical methods have been developed as a more general alternative, like the

QR technique (Anderson and Moore, 1985; King and Watson, 1998) and the QZ method (Sims,

1996; Uhlig, 1999; Christiano, 2002) which applies the stability criterion to a companion version of

the original structural model in order to exclude potential solutions which never converge to the

steady-state. To develop practical research skills these numerical methods will be applied to solve a

linear rational expectation model using MATLAB. A simple model is used to compare methods

currently available.

High Dimensional Covariance Estimation – Mohsen Pourahmadi (Texas A&M U.) – 3 credits

To be defined soon.

Macroeconomic Forecasting – Toni Espasa (U. Carlos III, Madrid) – 3 credits

This course deals with the recent evolution, perspectives and some policy considerations for the

Euro-Zone on the basis of the analysis of inflation, GDP and Industrial Production in EMU.

Multivariate Time Series – Gianluca Cubadda - 3 credits

Stationary and Ergodic Multivariate Time Series. Multivariate Wold Representation. Vector

Autoregression (VAR) Models. Identification and Estimation of VAR models. Forecasting.

Structural VAR Models. Impulse Response Functions. Forecast Error Variance Decompositions.

Shocks Identification Using the Choleski Factorization. The Cointegrated VAR. Maximum

Likelihood Inference on the Cointegrated VAR. The Common Trends Representation.

MICROECONOMICS 2

Advanced Topics in Economics - tba - 3 credits

This course is devoted to the study of the most advanced topics in economics presenting a selection

of the most debated and policy relevant topics. The choice of the topics and the structure of the

course will depend on the instructor who will typically be a visiting scholar.

Theory of Incentives – Andrea Attar – 3 credits

The lectures will focus on some recent developments in the theory of incentives. The building block

of the course is the basic principal-agent setting analyzed, for example, in Laffont Martimort

(2002). Possible extensions include: Auction Theory, Collusion Theory, Incentives and Competition

in markets subject to asymmetric information, Competition among principals, General Equilibrium

Theory under Asymmetric Information.

Information Economics, Game Theory and Auctions – Tommaso Valletti - 3 credits

Adverse selection, signalling and screening; Applications: education, product quality, insurance;

Moral hazard and the principal-agent problem. Imperfect Competition and Game Theory: Imperfect

Competition; Game Theory. Auctions. Independent private value.

MICROECONOMETRICS

How to write in International Journals – Gianni De Fraja – 1 credit

This seminar deals with strategies for selecting peer reviewed journals, the submission and revision

process and ultimately with writing and publishing good papers.

Microeconometrics using Stata – Melvyn Weeks (U. Cambridge) - 3 credits

Linear Models: Topics covered include the linear regression model, programme evaluation and

treatment effects, instrumental variables, static and dynamic panel Data models, and Generalised

Method of Moments. Nonlinear Models: Topics covered include Random Utility Models, Binary

and Multinomial Choice, Willingness-To-Pay Models, Dynamic Binary Choice Models and Count

Data Models.

PUBLIC ECONOMICS

International Economics – Luisa Corrado – 3 credits

The course offers a compendium between case-studies and the theory of international trade and

international finance. Special attention will be devoted to issues that have attracted increasing

attention in international economics. The course provides the theoretical background to understand

and address the main issues in the international economic debate. The course objectives are

complementary with potential students’ placement in international institutions (IMF, World Bank),

Central Banks, Research Divisions etc. Topics in International Finance: Exchange Rate Regimes.

Currency Crises. Financial Crises. Sovereign and Public Debt Crises. The Subprime Crisis.

Liquidity, Banks Leverage and the Macroeconomics. Topics in International Trade: Comparative

Advantages and the New Economic Geography. International Convergence and Growth. Topics in

European Economics: European Income Inequality. Regional Convergence Clubs. The EU Growth

Dilemma, Fiscal Compact and the Stability and Growth Pact. The EU Monetary and Fiscal Policy.

Prerequisites: Foundations in International Trade, International Monetary Economics and

International Finance.

Environmental Economics – Mariangela Zoli – 3 credits

The sources of environmental problems: property rights and externalities. Pollution: efficient targets

and policy responses. Climate change issues. Dynamic efficiency and sustainable development.

Energy issues. Waste management and policies.

Topics in Economics of the Environment – Alessio D’Amato – 3 credits

The aim of this module is to present several hot topics related to environmental and natural resource

economics. By following this course, students will discover currently debated issues concerning the

linkages between economic activities and environmental impacts; further, they will be able to

identify relevant behavioural drivers and related policy remedies, in realistic contexts where

noncompliance, intrinsic motivations and complex policies interactions are explicitly accounted for.

Topics on Procurement of Public Services – Elisabetta Iossa, Giancarlo Spagnolo – 3 credits

In house Provision vs Outsourcing of Public Services: Incomplete contracts; Public Private

Partnerships (PPP): Main characteristics; PPPs vs traditional procurement; Risk allocation in

PPPs;Case studies: London Underground; Prisons; Students accommodations; Providing Incentives

to Private Contractors: Explicit contracts (fixed price, cost plus and incentive contracts); Implicit

contracts and relational contracts: the importance of reputation. Tariff Regulation of Public

Services: Price cap; Rate of returns Regulation. Tender Design: Strategic design; Time incentives

and award criteria; Abnormally low tenders. Bid Rigging in Public Procurement: Incentives to

collude; Red flags. Corruption in public procurement: How to measure corruption; How to fight

corruption.

Welfare, Inequality and Poverty Measurement – Giovanni Vecchi - 3 credits

To be confirmed.