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Course 2020-2021 a.y.

30166 - TIME SERIES ECONOMETRICS

Department of Economics

Course taught in English

Go to class group/s: 31

CLEAM (6 credits - II sem. - OP  |  SECS-P/05) - CLEF (6 credits - II sem. - OP  |  SECS-P/05) - CLEACC (6 credits - II sem. - OP  |  SECS-P/05) - BESS-CLES (6 credits - II sem. - OP  |  SECS-P/05) - WBB (6 credits - II sem. - OP  |  SECS-P/05) - BIEF (6 credits - II sem. - OP  |  SECS-P/05) - BIEM (6 credits - II sem. - OP  |  SECS-P/05) - BIG (6 credits - II sem. - OP  |  SECS-P/05) - BEMACS (6 credits - II sem. - OP  |  SECS-P/05)
Course Director:
BARBARA CHIZZOLINI

Classes: 31 (II sem.)
Instructors:
Class 31: BARBARA CHIZZOLINI


Suggested background knowledge

Strongly suggested: introductory statistics and calculus (first year mathematics). A basic knowledge of Matrix Algebra could also help, but is not strictly necessary.


Mission & Content Summary
MISSION

The course is an introduction to Econometrics, with a focus on time series analysis and estimation techniques, applied to Macroeconomics. All topics are presented in theory and in practice: classroom lectures are mixed with computer lab sessions where applications in micro and macroeconomics are developed using Eviews, an econometrics software. Econometrics is a tool that students in all fields of economics and management can use to give an empirical framework to whatever report they need to write for their university courses or final dissertation, or even in their future job environment.

CONTENT SUMMARY

The main topics of the course are:

The linear regression model: the Ordinary Least Squares Estimator:

  • Estimation.
  • Properties and testing.
  • Specification and interpretation of estimated models.
  • Heteroskedasticity and GLS estimators.

Time Series:  univariate and multivariate dynamic models:

  • Stationary and White Noise time series.
  • Autoregressive models. The AR(1) model: properties, estimation, forecasting. 
  • Non stationary series. Unit roots: tests and properties.
  • VAR models.
  • GMM estimators: an introduction.

Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • Reach a medium-high knowledge of Linear Regression techniques, of the properties and use of the estimators.
  • Be able to specify and estimate simple empirical models derived from theoretical a-prioris (Economics, Management, Sociology, Political Economy...).
  • Use of Econometric softwares.
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...

Be confident enough in the use of econometric softwares to:

  • Produce descriptive statistics, graphs and other preliminary empirical analyses with any type of data.
  • Perform the estimation of linear models.
  • Write a report based on the outputs of the previous analyses.

Teaching methods
  • Face-to-face lectures
  • Online lectures
  • Individual assignments
DETAILS
  • Individual assignments: the students need to download from the Net, relevant data (mainly financial time series such as Stock Prices and Exchange Rates), perform some analyses using the appropriate econometric software, write and submit a report. 

Assessment methods
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  •     x
  • Individual assignment (report, exercise, presentation, project work etc.)
  •   x  
    ATTENDING AND NOT ATTENDING STUDENTS
    • The written examination is necessary to test the knowledge of the estimation techniques taught in the course.
    • Individual assignments are needed to test the level of ability reached in applying econometric techniques to  data, and to test the competence in the use of softwares.  

    Teaching materials
    ATTENDING AND NOT ATTENDING STUDENTS
    • Chris BROOKS, Introductory Econometrics for Finance, 4th edition, Cambridge U.P., 2019
    • F.X. DIEBOLD, Forecasting (online textbook at: http://www.ssc.upenn.edu/~fdiebold/Textbooks.html).
    • F.X. DIEBOLD, Time Series Econometrics (online textbook at: http://www.ssc.upenn.edu/~fdiebold/Textbooks.html).
    • Eviews Manual (online).                                                                  
    Last change 09/12/2020 16:46