DRE 7002 Time Series Econometrics

DRE 7002 Time Series Econometrics

Course code: 
DRE 7002
Course coordinator: 
Hilde Christiane Bjørnland
Course name in Norwegian: 
Time Series Econometrics
Product category: 
PhD Economics courses
2017 Autumn
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

Please note that the course will be revised before it is offered again.

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. If time permits, we wil also cover estimation of DSGE models (topic VI).

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 and sign restrictions

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. Estimating DSGE models

  • Bayesian estimation
  • Local approximation of linearized models
Learning process and requirements to students

The course will we taught in three intensive modules. Each module consists of 2*5 hours (2 days and 5 hours per day). 

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
All components must, as a main rule, be retaken during 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:All components must, as a main rule, be retaken during next scheduled course
Exam organisation: 
Ordinary examination
Total weight: 
Student workload
Group work / Assignments
75 Hour(s)
Specified learning activities (including reading).
Student's own work with learning resources
75 Hour(s)
Autonomous student learning (including exam preparation).
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.