This course is discontinued

ECON5125 - Time series econometrics for non-stationary variables

Schedule, syllabus and examination date

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Course content

The course deals with econometric modelling, estimation, and testing of relationships and models for time series data with main focus on methods for handling non-stationary time series.

Necessary technical background will be established that includes introduction to stationarity and non-stationarity concepts, ARMA- and VAR-modelling, deterministic and stochastic trends, integrated and cointegrated variables, unit roots, identification and exogeneity. Consequences for estimation and interpretation of econometric models that may include non-stationary variables will be looked at. Both single relation methods and the multi-relation system approach will be discussed as well as statistical methods for estimating and determining the presence of one or more cointegrated relations among a set of economic time series.

In addition to discussing the importance of VAR-models for econometric modelling with special focus on autoregressive distributed lag- and error correction models, the course emphasizes applications concerning modelling, estimation, policy analysis and forecasting.

Additional information and regulations about teaching and exams

Learning outcome

1. The students should know basic econometric terminology and estimation and test principles for time series models and data. They should be able to formulate and estimate dynamic single-equation and multi-equation models for stationary as well as non-stationary time series data, to interpret such models, and to know how they can be used for simulation purposes and for examining forecasting errors.

2. They should be able to estimate and interpret, in precise econometric terms, the difference between long-run and short-run effects, as well as deterministic and stochastic trends.

3. The students should know the basic theory, including the Granger Representation Theorem, and estimation and testing for the statistical Vector Autoregressive model (VAR), and understand its relevance for the modelling of econometric dynamic systems. They should be able to test the presence of cointegrated relationships in a system, determine empirically the number of such relationships and to use and interpret output from relevant software.

Admission

Students who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for in Studentweb.

Students enrolled in other Master's Degree Programmes can, on application, be admitted to the course if this is cleared by their own study programme.

If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures.

The subject is open for both Norwegian and international students.

Prerequisites

Formal prerequisite knowledge

Bachelor's degree in Economics, or equivalent.

Recommended previous knowledge

ECON4160 Econometrics - Modelling and systems estimation. Basic knowledge of matrix algebra.

Overlapping courses

SØK 711 Økonometri avanserte emner: Tidsserieanalyse

Teaching

Lectures: 2 hours per week throughout the semester. Seminars: 2 hours per week through parts of the semester.

Term papers, inter alia with use of PC (compulsory).

Examination

3 hours written school exam. Students are not allowed to present themselves to the written exam if not at least one term paper is accepted.

Resources allowed: Open book examination where all printed and written resources, in addition to calculator, are allowed.

Resit an examination

The Department of Economics has passed following resolution for ECON-courses: It will no longer be possible for candidates to register for an exam in a lower level course after having passed exams in intermediate and advanced level courses in the same subject area (also where there are no pre-requisites that apply to the intermediate course). Further information can be found here.

Facts about this course

Credits

10

Level

PhD

Teaching

Spring 2008

Examination

Spring 2008

Teaching language

English