ECTS - University of Pisa
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TOPICS IN MACROECONOMETRICS
GIUSEPPE RAGUSA
Academic year2021/22
CourseECONOMICS
Code610PP
Credits6
PeriodSemester 2
LanguageItalian

ModulesAreaTypeHoursTeacher(s)
TOPICS IN MACROECONOMETRICSSECS-P/05LEZIONI42
GIUSEPPE RAGUSA unimap
Obiettivi di apprendimento
Learning outcomes
Conoscenze

Topics in Macroeconometrics si rivolge agli studenti che desiderano acquisire una conoscenza pratica dei metodi moderni utilizzati in macroeconomia e in finanza.

 
Knowledge

Topics in Macroeconometrics aim is to endow students with a working knowledge of the modern econometric methods used in macroeconomics and, to some extent, in finance.

Topics range from classic time series concepts such as linear univariate and multivariate processes (ARMA, VAR) to techniques that have only recently entered the applied macroeconomist' toolbox (Bayesian VAR and Factor Models).


Students need to be familiar with econometric theory at the level of Advanced Econometrics (246PP).
Modalità di verifica delle conoscenze

Per l'accertamento delle conoscenze saranno svolte delle prove in itinere.
Assessment criteria of knowledge

Ongoing assessment to monitor academic progress will be carried out.
Capacità

Gli studenti acquisiranno una conoscenza dell'econometria delle serie temporali che va dagli strumenti classici come i processi stazionari lineari (ARMA, VAR) alle tecniche che sono recentemente entrate nella cassetta degli attrezzi macroeconomista (econometria bayesiana, kalman filter, factor models)
Skills

By the end of the course, students should be able to:

1. apply the techniques introduced in class to real data using Julia

2. Have a clear picture of the challenges of identification in macroeconomics

3. Be able to construct state-of-the-art forecasting models
Modalità di verifica delle capacità

Alcune lezioni saranno dedicate alle appliazioni empiriche per le quali sarà usato il software statistico Julia.
Assessment criteria of skills

Some lectures will be devoted to empirical applications and will require the use of the
statistical software Julia
Behaviors

Attendance It is expected that all students attend the lectures, be up to date with their readings and be prepared to participate fully in class. If you have problems mastering the material covered in class, please ask questions in class or during office hours. Cheating and other forms of dishonesty I have no tolerance for cheating. I regard academic dishonesty as a very serious offense. Students caught cheating during exams will fail the class and will be reported to the appropriate officer of the college.
Modalità di verifica dei comportamenti

Frequenza

La frequenza alle lezioni è consigliata, così come una preparazione per una piena partecipazione alla lezione. Gli studenti che hanno problemi possono fare domande durante la lezione o durante l'orario di ricevimento.

Imbrogli o altre forme di disonestà

Non ci saranno tolleranze nei confronti di qualsiasi tipo di imbroglio. Gli studenti che saranno trovati a copiare non supereranno l'esame e il loro comportamento sarà riportato agli uffici competenti.
Prerequisiti (conoscenze iniziali)

Advanced Econometrics (246PP)
Prerequisites

Students need to be familiar with econometric theory at the level of Advanced Econometrics (246PP).
Teaching methods

Lectures and interactive sessions
Syllabus
  1. From Cross-Section to Time Series: asymptotic theory under serial correlation
  2. Stationary Process
    1. Linear processes
    2. The Wold representation theorem
    3. ARMA processes: estimation, and forecasting
    4. ARIMA models for non-stationary time series
  3. Multivariate Time Series
    1. Vector Auto-Regressions (VAR)
    2. Structural VARs: identification
    3. Impulse responses
    4. Applications: Fiscal multiplier; Monetary Policy multiplier.
  4. The Bayesian paradigm
    1. Likelihood, prior, and posterior
    2. Bayesian computations
    3. Applications: Bayesian VAR
  5. Factor models and High Dimensional Econometrics
    1. Principal components
    2. Dynamic factor models
    3. Ridge and Lasso
Bibliografia e materiale didattico

The main reference for this courses is:

  • Brockwell, Peter J. and Richard A. Davis, Introduction to Time Series and Forecasting, Springer, 2002
  • Enders, Walter. Applied econometric time series. John Wiley & Sons, 2008
  • James D. Hamilton, Time Series Analysis, Princeton University Press, 2005
Bibliography

The main reference for this courses is:

  • Brockwell, Peter J. and Richard A. Davis, Introduction to Time Series and Forecasting, Springer, 2002
  • Enders, Walter. Applied econometric time series. John Wiley & Sons, 2008
  • James D. Hamilton, Time Series Analysis, Princeton University Press, 2005

The instructor will provide handouts and other reading material to highlight challenging aspects of a topic or present material not covered by the main references.

 
Non-attending students info

The class has is own Teams channel. I will use it extensively for important communication.

The class has also a website https://gragusa.org/macrotopics. I will use it to host the course material.
Modalità d'esame

La valutazione e' basata interamente su homework assignments.
Assessment methods

The final grade depends on several homework assignments.
Additional web pages

https://gragusa.org/macrotopics
Updated: 17/02/2022 20:24