Macroeconometrics: Advanced Time-Series Analysis

Objectif

This in-depth macroeconometrics course covers advanced multivariate timeseries methods for forecasting and structural analysis. Both frequentist and Bayesian approaches for estimating VARs for stationary and non-stationary processes will be explored, along with various macroeconomic priors. The course will also cover statistical and IV methods for identifying structural shocks, and how they can be applied to macro problems. By the end of the course, students will have a comprehensive understanding of advanced multivariate time-series techniques and their practical application to empirical research.

A substantial part of the course will be based on papers. No specific text is recommended.

The exam will be a discussion in class of group projects. Each group will be assigned a paper to replicate, extend and discuss.

Exam guidance

Each group is expected to submit a report and a replication code, clean and running a week before the first round of oral discussion.

The report should contain:

- A summary of the paper;

- Replication of the main results (secondary results/results in the appendix can be skipped);

- A meaningful discussion on the potential issues in the paper, or some extension or application.

For the discussion, you should prepare a short presentation (15-20 min) on the work done. All the components of the group should be able to present or should alternate in presenting. The class will be asking questions. 

Plan

1. Estimation of VARs
2. Bayesian VAR, macroeconomic priors and hyperpriors
3. Trends, unit roots, and co-integration
4. Large VARs and big datasets
5. Structural VARs and Local Projections
6. IV methods in macroeconomics
7. Macroeconomic Shocks

Références

For time-series analysis helpful references are Hamilton (1994), Kilian and
Lütkepohl (2017) and the notes of Cochrane (2005). For those interested,
more technical books are Brockwell and Davis (1991), Hannan (1970) and
Madsen (2008).
Zellner (1996) and Kim and Nelson (1999) are useful references for Bayesian
methods. Del Negro and Schorfheide (2011) provides a very good introduction
to Bayesian methods in macro. Students may also want to have a look
at the two chapters of the Oxford Research Encyclopedia of Economics and
Finance on BVARs (see Miranda-Agrippino and Ricco (2019b,a)).
Structural VARs are discussed in Canova (2007) and Kilian and Lütkepohl
(2017). A comprehensive review article on macroeconomic shocks is Ramey
(2015) in the Handbook of Macroeconomics.

Macroeconomic Data:
Lequiller and Blades (2014) – “Understanding National Accounts”, Mc-
Culla and Smith (n.d.) – ‘A Primer on GDP and the National Income
and Product Accounts’
– Financial Sector: Basu et al. (2011), Colangelo and Inklaar
(2012)
– Hedonic Measures: Wasshausen and Moulton (2006), Gordon
(2006)

– Discrepancies & Measurement Error: Manski (2015), Landefeld
et al. (2008), Aruoba et al. (2013).
– Data Revisions: Arouba (2008)
– Seasonality: Wright (2013)
– FRED dataset McCracken and Ng (2015)

Bayesian Vector Autoregressions
Doan et al. (1983), Sims and Zha (1998), Del Negro and Schorfheide
(2011), Miranda-Agrippino and Ricco (2019b), Miranda-Agrippino and
Ricco (2019a) Ba?bura et al. (2010), Giannone et al. (2012), Karlsson
(2013)
2 Hyperparameters selection and Long-Run priors
Sims (1996), Sims (2000), Giannone et al. (2015), Giannone et al.
(2019)
2 Large VARs and Big Data
Stock and Watson (2006), De Mol et al. (2008), Ba?bura et al. (2010),
Hastie et al. (2001, 2015), Ellahie and Ricco (2017), Giannone et al.
(2021)

IV methods in macro
Stock (2008), Stock and Watson (2012), Mertens and Ravn (2013),
Miranda-Agrippino and Ricco (2023), Forni et al. (2022)
2 Non-Fundamentalness
Lütkepohl (2012), Lippi and Reichlin (1994), Alessi et al. (2011), Beaudry
et al. (2015), Forni and Gambetti (2014), Forni et al. (2013), Mertens
and Ravn (2010), Leeper et al. (2013), Forni and Gambetti (2014)
2 Local Projections
Jordà (2005), Kilian and Kim (2011), Marcellino et al. (2006), Chevillon
(2007), Plagborg-Moller (2019), Plagborg-Møller and Wolf (2021), Li
et al. (2022)
2 Statistical approaches to structural Identification
Ramey (2015), Sims (1980), Stock and Watson (2001), Kilian (2011),
Cochrane (1994).
– Short-Run, Medium-Run, Long-Run Restrictions: Beaudry
and Portier (2006), Ben Zeev and Pappa (2014), Blanchard
and Quah (1989), Blanchard and Perotti (2002), Barsky and Sims
(2012)

– Sign Restrictions: Uhlig (2005), Arias et al. (2014), Arias et
al. (2014), Baumeister and Hamilton (2014)
– Heteroskedasticity: Rigobon (2003), Brunnermeier et al. (2021)
2 Narrative Methods & High Frequency Instruments Ramey
(2015), Romer and Romer (2004), Romer and Romer (2010), Gürkaynak
et al. (2005), Coibion (2012), Ramey and Zubairy (2018), Gertler and
Karadi (2015), Jaroci?ski and Karadi (2020), Miranda-Agrippino and
Ricco (2021), Swanson (2020), Känzig (2021)