NMAA05025U Econometrics 2: Statistic Analysis of Econometric Time Series (StatØ2)
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.
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.
- Category
- Hours
- Exam
- 35
- Lectures
- 35
- Preparation
- 90
- Project work
- 25
- Theory exercises
- 21
- Total
- 206
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- 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 Master
- Duration
- 1 block
- Placement
- Block 1
- Schedule
- C (Mon 13-17 + Wednes 8-17)
- 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)