DRE 7002 Time Series Econometrics

DRE 7002 Time Series Econometrics

Course code: 
DRE 7002
Course coordinator: 
Hilde Christiane Bjørnland
Product category: 
PhD Economics courses
2019 Spring
Active status: 
Teaching language: 
Course type: 
One semester

The aim of the course is to give the students a formal understanding of time series econometrics at a level expected among Ph.D students in economics, finance and related disciplines.

Learning outcomes - Knowledge

After taking this course students should have a solid knowledge of the basic techniques used in time series econometrics, so that eventually they can master and produce sophisticated applied econometric analysis. The students will learn univariate and multivariate models of stationary and nonstationary time series, including structural VARs, state space models, the Kalman filter and factor models. The students will learn to master the main estimation methods, such as maximal likelihood, instrumental variables and GMM. The students will also learn Bayesian estimation.

Course content

I. Univariate stationary time series

  • Stationary AR and MA processes
  • Forecasting
  • Spectral analysis

II. Models of non-stationary time series

  • Deterministic and stochastic trends, unit root tests, structural change
  • Trend/cycle decompositions (linear filters)
  • Analysis of business cycles in the frequency domain, spurious cycles

III. Vector autoregression (VAR) methodology

  • Granger causality, cointegration.
  • Structural VARs – impulse responses, forecast error variance decomposition
  • Identification: Cholesky, long-run restrictions, sign restrictions, external instruments.

IV. Methods of Estimation

  • Instrumental variables (IV) estimation
  • Maximum likelihood estimation
  • Generalized method of moments (GMM) estimation

V. State space models and the Kalman filter

  • Kalman filter
  • Factor models

VI. Bayesian estimation

Learning process and requirements to students

Students are required to participate in class – both in discussions and by presenting models/material from the reading lists –  as well as solve and hand in solutions to exercises and problems.

Software tools

Enrollment in a PhD programme is a general requirement for participation in PhD courses at BI Norwegian Business School.
External candidates are kindly asked to attach confirmation of enrollment in a PhD programme when signing up for a course. Other candidates may be allowed to sit in on courses by approval of the course leader. Sitting in on a course does not permit registration for the course, handing in exams or gaining credits for the course. Course certificates or confirmation letters will not be issued for sitting in on courses.

Exam categoryWeightInvigilationDurationGroupingComment exam
Exam category:
Form of assessment:
Written submission
Exam code:
Grading scale:
Grading rules:
Internal and external examiner
Examination when next scheduled course
100No30 Hour(s)Individual
Exam category:Submission
Form of assessment:Written submission
Grouping (size):Individual
Duration:30 Hour(s)
Exam code:DRE70021
Grading scale:Pass/fail
Resit:Examination when next scheduled course
Exam organisation: 
Ordinary examination
Total weight: 
Workload activityDurationType of durationComment student effort
Group work / Assignments75Hour(s)Specified learning activities (including reading).
Self study75Hour(s)Autonomous student learning (including exam preparation).
Expected student effort:
Workload activity:Group work / Assignments
Duration:75 Hour(s)
Comment:Specified learning activities (including reading).
Workload activity:Self study
Duration:75 Hour(s)
Comment:Autonomous student learning (including exam preparation).
Workload activity:Teaching
Duration:30 Hour(s)
Sum workload: 

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.

Talis literature


Authors/Editors År Tittel Edition Publisher StudentNote
Bjørnland, Hilde Christiane; Thorsrud, Leif Anders 2015 Applied time series for macroeconomics 2. utg Gyldendal akademisk  
Hamilton, James D. 1994 Time series analysis   Princeton University Press  
Kim, Chang-Jin; Nelson, Charles R. cop. 1999 State-space models with regime switching: classical and Gibbs-sampling approaches with applications   MIT Press  


Authors/Editors År Tittel Edition Publisher StudentNote
Favero, Carlo A. 2001 Applied macroeconometrics   Oxford University Press Chapter 1,2,3, 6, 7 and 8.
Lütkepohl, Helmut cop. 2005 New introduction to multiple time series analysis   Springer  

No importance set

No type set
Authors/Editors År Tittel Edition Publisher StudentNote

Notes to students:

During the course there may be hand-outs and other material on additional topics relevant for the course and the examination.