(Advanced) Time Series Analysis 2011-2012

Classes on Monday 10h-13h (AV 03.12);  Teacher: Christophe Croux;  pdf-file course notes (notes2012.pdf).

All computer sessions take place in the pc-rooms in the library of the FEB (Naamsestraat 69).  We work with the software package Eviews. Working from home is possible, using Connecting to the student Remote Desktop Server from a public network.pdf. The sessions take place on (i) Friday, 13h-15h for the students in Economics; (ii) on Thursday, 12h-14h, for the students Business Economics and  the preparatory program Financial Economics and Business Economics (iii) Thursday, 18h-20h for the students taking the Advanced course (iv) the other students joint one of the groups on Friday, 13h-15h of Thursday, 12h-14h.

There is an individual paper  to write  in the middle of the semester. You need to analyze a time series  using techniques from the course.  The paper needs to be 5 to 10 pages long, summarizing  the most important results.  Some guidelines: (i) discuss briefly what the time series presents and give the source of the data (ii)  start with a univariate time series analysis  (iii) show that you can also  handle a multivariate problem  (iii) add a forecast (iv) add only the most important figures/tables in your homework, you can summarize the other ones (v) what have you learnt, as an economist/forecaster?

Evaluation (students  not taking advance time series analysis)  At the defense, your paper will be discussed with the examiner. General questions on the course contents will also be asked. Take your exam paper with you. Send the .pdf or .doc document to  Steffi.Frison[at]econ.kuleuven.be, with subject 'paper time series', before your course evaluation takes place.  Attach  the data set if the source description of the data is not sufficiently accurate. The evaluation takes place in the week of November 14.

Evaluation (students taking advance time series analysis)  At the defense, your paper will be discussed with the examiner. General and specific  questions on the course contents will also be asked. Take your exam paper with you. Send the .pdf or .doc document to Jonathan.Cornelissen[at]econ.kuleuven.be econ.kuleuven.be, with subject 'paper time series', before your course evaluation takes place.  Attach  the data set if the source description of the data  is not sufficiently accurate. The evaluation takes place in the week of December 5.

Course Outline

Week of 3 October: General Introduction ; AR models

Week of 10 October: ARIMA models ; Forecasting +  COMPUTER SESSION

Week of 17 October:  Testing for unit roots ; SARIMA  + COMPUTER SESSION

Week of 24 October : Model comparison ;  Dynamic Models + COMPUTER SESSION

Correction Homework 2 Advanced course: correction.pdf

Week of 31 October:  Introduction to Cointegration; + COMPUTER SESSION

Advanced: week of 7 November: GARCH ; VAR models

Advanced: week of 14 November: VECM  models + COMPUTER SESSION

Advanced: week of 21 November: mathematical complements

Advanced: week of 28 November:  presentation paper in class

Data sets (by chapter):

  1. chap1a.wf1 liquor.wf1   file1b.wf1
  2. assvie.wf1, mag.wf1
  3. hs.wf1, assvie.wf1
  4. ger.wf1
  5. csgdp.wf1
  6. csgdp.wf1
  7. housing.wf1  ; varexample.wf1
  8. emplaus.wf1, dowaus.wf1, ppp.wf1

Data sets in Excel format: demo.xls   chap1a.xls, file1b.xls, assvie.xls, mag.xls, hs.xls, csgdp.xls, ger.xls, emplaus.xlsvarexample.xls

 

References

 

Most  econometrics textbooks contain chapters on time series analysis. For example:

Stock and Watson, Introduction to econometrics (2nd edition), 2007. [Chapter 14 and 16] Students having lack of knowledge in econometrics may consult this book for the other chapters.

 

There are existing plenty of specialized books on time series analysis. Examples of books that are accessible  by undergraduate students are

Diebold, F.X., Elements of Forecasting (2nd edition), 2001.

Brockwell, P.J., and Davis, R.A., Introduction to Time series and Forecasting (2nd edition), 2002.

Enders, W., Applied Econometric Time series, 2004.

Hamilton, J.D., Time Series Analysis, 1994.