NMAA05025U Econometrics 2: Statistic Analysis of Econometric Time Series (StatØ2)
Volume 2013/2014
Education
MSc programme in
Mathematics-Economics
MSc programme in Statistics
MSc programme in Statistics
Content
The course
introduces and analyzes models and statistical procedures for
multivariate observations that are dependent over time. Examples of
such data are interest rates and stock prices. The focus is on the
autoregressive (AR) model and its multivariate version (VAR),
including unit root inference and cointegration analysis. A brief
introdution to related non-linear models (e.g. the ARCH-model) is
also given. The
probability theory and other mathematics necessary to analyze the
models and estimation and test procedures is presented. The
topics from probability theory include martingales, Markov chains,
asymptotic stability, stationarity, mixing, and laws of large
number and central limit theorems for time dependt processes.
By means of the
methods presented in the course, the students will solve
theoretical econometric problems and use statistical software to
analyse economic time series.
Learning Outcome
Knowledge: The course
covers the following topics. Dependence and correlation, stationary
and mixing stochastic processes, laws of large numbers for
dependent sequences, martingales, central limit theorems for
martingales, Markov processes, asymptotic stability, linear
processes, uni- and multivariate autoregressive processes,
estimation and asymptotic statistical theory for time series
models, exogeneity, tests for misspecification of time series
models, non-linear time series models, autoregressive processes
with unit roots, cointegration.
Skills: After the course, the students are expected to be able to apply the key time series models typically used for analysis of macro econometric data, to use statistical software for time series analysis, apply key concepts and methods from the theory of stochastic processes (including martingales, laws of large numbers and central limit theorems) to analyse statistical methods for time series, to formulate and apply likelihood based tests for linear hypotheses and specification tests for time series models, and to determine whether or not a stochastic process is exogenous.
Competences: After the course, the students are expected to be able to analyse macro economic time series statistically at an advanced level and to make predictions of future values of the series, and to be able to theoretically analyse uni- and multivariate time series models and to develop statistical methodology for such models.
Skills: After the course, the students are expected to be able to apply the key time series models typically used for analysis of macro econometric data, to use statistical software for time series analysis, apply key concepts and methods from the theory of stochastic processes (including martingales, laws of large numbers and central limit theorems) to analyse statistical methods for time series, to formulate and apply likelihood based tests for linear hypotheses and specification tests for time series models, and to determine whether or not a stochastic process is exogenous.
Competences: After the course, the students are expected to be able to analyse macro economic time series statistically at an advanced level and to make predictions of future values of the series, and to be able to theoretically analyse uni- and multivariate time series models and to develop statistical methodology for such models.
Academic qualifications
Stat2 or
equivalent.
Teaching and learning methods
5 hours of lectures and 3
hours of exercises per week for 7 weeks.
Workload
- Category
- Hours
- Exam
- 35
- Lectures
- 35
- Preparation
- 90
- Project work
- 25
- Theory exercises
- 21
- Total
- 206
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Exam
- Credit
- 7,5 ECTS
- Type of assessment
- Written examination, 3 hours under invigilation---
- Exam registration requirements
- 2 compulsory written assignments must be handed in and approved.
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
One internal examiner
- Re-exam
- 30 minutes oral exam with several internal examiners, 7-point grading scale. Compulsory written assignments from the course that are approved and valid do not need to be repeated. Compulsory assignments that have not been approved or are invalid must be handed in no later than one week before the start of the re-exam period.
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome.
Course information
- Language
- English
- Course code
- NMAA05025U
- Credit
- 7,5 ECTS
- Level
- Full Degree MasterBachelor
- Duration
- 1 block
- Placement
- Block 1
- Schedule
- C
- Course capacity
- No limit
- Continuing and further education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Course responsibles
- Michael Sørensen (7-716d676c656970447165786c326f7932686f)
Phone +45 35 32 06 80, room
04.3.13
Saved on the
30-04-2013