DRE 7002 Time Series Econometrics

DRE 7002 Time Series Econometrics

Course code: 
DRE 7002
Department: 
Economics
Credits: 
6
Course coordinator: 
Hilde Christiane Bjørnland
Product category: 
PhD
Portfolio: 
PhD Economics courses
Semester: 
2019 Spring
Active status: 
Active
Teaching language: 
English
Course type: 
One semester
Introduction

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
Matlab
Rats
Qualifications

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:
Submission
Form of assessment:
Written submission
Exam code:
DRE70021
Grading scale:
Pass/fail
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
100No30 Hour(s)Individual
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:100
Invigilation:No
Grouping (size):Individual
Duration:30 Hour(s)
Comment:
Exam code:DRE70021
Grading scale:Pass/fail
Resit:Examination when next scheduled course
Exam organisation: 
Ordinary examination
Total weight: 
100
Workload activityDurationType of durationComment student effort
Group work / Assignments75Hour(s)Specified learning activities (including reading).
Self study75Hour(s)Autonomous student learning (including exam preparation).
Teaching30Hour(s)
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)
Comment:
Sum workload: 
180

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.