ECTS - University of Pisa
Scheda programma d'esame
TIME SERIES ECONOMETRICS
GIUSEPPE RAGUSA
Academic year2019/20
CourseECONOMICS
Code247PP
Credits6
PeriodSemester 2
LanguageEnglish

ModulesAreaTypeHoursTeacher(s)
TIME SERIES ECONOMETRICS SECS-P/05LEZIONI42
GIUSEPPE RAGUSA unimap
Obiettivi di apprendimento
Learning outcomes
Conoscenze

Time Series Econometrics si rivolge agli studenti che desiderano acquisire una conoscenza pratica dei metodi moderni utilizzati in macroeconomia e in finanza.

 
Knowledge

Time Series Econometrics is aimed at students who wish to gain a working knowledge of the modern methods used in macroeconomics and to some extent in finance.   By the end of the course, students will have gained a good understanding of time series econometrics ranging from classic tools such as linear stationary processes (ARMA, VAR) to techniques that have recently entered the macroeconomist toolbox (Bayesian and high dimensional estimation).
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 high dimensional econometrics).
Skills

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

 

1. describe and verify mathematical considerations for analyzing time series, including concepts of white noise, stationarity, autocovariance, autocorrelation

2. apply various techniques of time series models

3. apply various techniques for the modeling: including parameter estimation and assumption verification, 

5. describe and apply techniques of selected additional topics, such as state-space models, multivariate time series, factor models

 

6. Use Julia
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
Programma (contenuti dell'insegnamento)
  1. From Cross-Section to Time Series: asymptotic theory under serial correlation
  2. Stationary Process
    1. Linear processes
    2. The Wold decomposition
    3. ARMA processes
      1. Representation. Estimation. Forecasting. Applications.
  3. Non-stationary Time Series Model
    1. ARIMA models for non-stationary time series
    2. Unit Roots
    3. Forecasting with ARIMA models
  4. Multivariate Time Series
    1. Vector Auto-Regressions (VAR)
    2. Modeling and Forecasting with VAR
    3. Cointegration
    4. Structural vs Reduced Form VAR: identification
  5. State-Space Models
    1. Linear State-Space models
    2. State-Space representation of ARIMA models
    3. The Kalman filter
    4. Parameter Estimation for State Space models
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. State-Space Models
    1. Linear State-Space models
    2. The Kalman filter
  6. Factor models and High Dimensional Econometrics
    1. Principal components
    2. Dynamic factor models
    3. Machine Learning methods for time series
    4. Application: forecasting with large datasets
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

However, especially for some of the topics, other references are going to be useful:

  • 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

However, especially for some of the topics, other references are going to be useful:

  • Enders, Walter. Applied econometric time series. John Wiley & Sons, 2008
  • James D. Hamilton, Time Series Analysis, Princeton University Press, 2005
Modalità d'esame

Il voto finale dipende dal voto su una serie di homework e sull'esame orale.
Assessment methods

The final grade depends on several homework assignments and an oral exam. Homework assignments are compulsory for successfully completing the course, and they will be valid for one academic year.

 
Updated: 20/05/2020 17:05