DRE 7002 Time Series Econometrics
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.
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.
I. Univariate stationary time series
- Stationary AR and MA processes
- 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
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.
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 category||Weight||Invigilation||Duration||Grouping||Comment exam|
Form of assessment:
Internal and external examiner
Examination when next scheduled course
|Form of assessment:||Written submission|
|Resit:||Examination when next scheduled course|
|Workload activity||Duration||Type of duration||Comment student effort|
|Group work / Assignments||75||Hour(s)||Specified learning activities (including reading).|
|Self study||75||Hour(s)||Autonomous student learning (including exam preparation).|
|Workload activity:||Group work / Assignments|
|Comment:||Specified learning activities (including reading).|
|Workload activity:||Self study|
|Comment:||Autonomous student learning (including exam preparation).|
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.
|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|
|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
Notes to students:
During the course there may be hand-outs and other material on additional topics relevant for the course and the examination.