90349 - Structural Macroeconometrics

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

1- Structural shocks

2- From small-scale monetary DSGE models to SVARs: Why Choleski-SVARs are not enough?

3- A few words on reduced form VAR representations and estimation issues (this can be skipped and deferred to Supplementary Material)

4- SVARs, Structural IRFs & Intuitive Approach to the Identification of SVARs

5 - AB-SVARs: specification, identification and estimation

     5.1 - Case study: Blanchard and Perotti's (2002, QJE) fiscal model

6 - FEVDs (short account)

7 - Analytic confidence bands for IRFs

8 - Bootstrap confidence bands for IRFs

9- More "recent" identiFIcation schemes

     9.1- The sign-restrictions approach: a frequentist view (short account)

     9.2 -The "statistical" ICA-approach (short account)

     9.3 - Heteroskedasticity approach: constant IRFs

     9.4 - Heteroskedasticity approach: regime-dependent IRFs

     9.5 - The "external variables" approach

              9.5.1-Proxy-SVARs, identification and estimation

              9.5.2-Local Projections

Readings/Bibliography

- Slides provided by the teacher available on Virtuale

- 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 "virtual labs" (i.e. the students bring their laptops in the classroom with freely or Unibo licenzed econometric softwares installed).

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.

Alternatively, the student can propose a topic of interest whose consistentcy with the course contents must be evaluated and approved by the teacher.

 

Grades of the form XX/30 are given. Overall, the meaning of grades is as follows

<18 failed
18-23 sufficient
24-27 good
28-30 very good
30 e lode excellent.

 

Should the course be held online (because of Pandemic issues etc.) the exam will still consist in writing a short paper on a project (related to the topics covered during classes) assigned by the teacher.  

 

Teaching tools

Software used are:

Gretl wich open source and is freely downloadle from the web

Matlab for which Unibo has a licence which means that students can download and freely install it on their laptops, etc.

Links to further information

https://www.unibo.it/sitoweb/luca.fanelli/en

Office hours

See the website of Luca Fanelli