Section outline

  • SCHEDULE - 

    Lecture:  Wednesday 9:00-11:00 am    Bancroft: 3.26                 (Liudas Giraitis)
    Class-Tutorial:  Thursday 11:00-12:00pm  QB 212 PC lab (Claudio Vallar)

    Week 1,   24 January,   Introduction to the course
    Week 2,   31 January, (lecture 1, no tutorial)                      )
    Week 3,   7 February, (lecture 2,  Tutorial 1)
    Week 4,  14 February (lecture 3,  Tutorial 2)
    Week 5,   21 February, (lecture 4,  Tutorial 3)
    Week 6,   28 February, (lecture 5,  Tutorial 4)

    Week 7, 6 March, Reading week [no teaching]

    Week 8,   13 March (lecture 6,  Tutorial 5)
    Week 9,    20 March (lecture 7, Tutorial 6)
    Week 10, 27 March Midterm test [9:00-10:00]  (+ Lecture 8, 45min]
    Week 11,  3 April    (lecture 9,  Tutorial  7)
    Week 12,  10 April   (lecture 10,  Tutorial general overview)

    The outline of the course 

    It is based on the following syllabus. Some of the topics will be carried over consecutive weeks: 

    1.  Graphical analysis, plots, impulse response  function

    2.  Introductory concepts. Stylized facts of financial data.  Summary statistics. Testing for symmetry and heavy tails. Stationarity, autocorrelation function, white noise sequence 

    3.  Testing for  correlation. AR, MA and ARMA models.

    4.  Linear time series: a) Autoregressive process. AR(1) and AR(2) models: properties and prediction, business cycle. Order determination. Estimation of an AR(1) model. Forecasting. 

    5.  Model selection and forecasting

    6.  Non-stationary processes, unit root models, testing for unit root.

    7. Seasonal time series models 

    8 . Introduction to conditional heteroscedasticity: ARCH, GARCH models.  

    9  Regression models with times series errors. Co-integration. Multivariate time series models