Course Unit Page


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education Gender equality Decent work and economic growth Industry, innovation and infrastructure

Academic Year 2020/2021

Learning outcomes

At the end of the course the student has acquired a comprehensive knowledge of the main identification and estimation methods which can be featured by Structural Vector Autoregressions (SVARs) in order to quantify the dynamic causal effects of macroeconomic structural shocks of interest including, among others, the monetary policy shock and uncertainty shocks. In particular, he/she is able to - analyze critically the implications of macroeconomic theories in terms of estimated impulse response functions, and to make inference on the identified dynamic causal effects; - apply SVAR analysis to Euro area and/or U.S. monthly/quarterly data by available econometric packages with the idea of replicating existing results or producing new ones.

Course contents

Introduction to the course: What is a shock? Macroeconomic shocks and their impact: impulse response functions (IRFs).

Examples: the impact of uncertainty shocks, the impact of monetary policy shocks, the impact of fiscal shocks.

Vector Autoregressions (VARs): representations and estimation issues (short account)

Structural Vector Autoregressions (SVARs):

- Cholesky-SVARs;

- B-model: identification and estimation;

- A-model: identification and estimation;

- AB-model: identification and estimation;

- Confidence bands for impulse responses: bootstrap methods (short account)

- Identification through long-run restrictions


Novel identification schemes:

- Sign restrictions (frequentist approach)

- Identification through heteroskedasticity

- Proxy-SVARs


Local Projections


- Slides provided by the teacher available on IOL.

- Kilian, L. and H. Lutkepohl (2017), Structural Vector Autoregressive Analysis, Cambridge University Press.

- Lutkepohl. H. (2015), New Introduction to Multivariate Time Series Analysis, Springer

- Amisano, G. and C. Giannini (1997), Topics in Structural VAR Econometrics, 2nd edn, Springer, Berlin.

Teaching methods

Traditional classes and labs: use of software Matlab and Gretl

Attending classes is crucial to fully understand the spirit of this course

Assessment methods

The exam aims to verfy that the student has achieved the basic ingredients necessary to quantify the impact of macroeconomic shocks on the macroeconomy by SVAR methods.

More in detail, the students is supposed to have acquired:

- the knowledge of VAR models as key tools to capture dynamic properties of macroeconomic variables;

- methods to address the identification problem implied by the SVAR methodology;

The student is also supposed to carry out independent empirical work.

The exam consists in writing a short paper on a project (related to the topics covered during classes) assigned by the teacher.

Grades of the form XX/30 are given.

Teaching tools

Labs and software

Online classes if necessary, according to the rules proovided by Teaching Service of the University

Office hours

See the website of Luca Fanelli