ECON5101 – Advanced Econometrics - Time series
The course deals with advanced econometric methods for estimation and testing of economic relationships using time series data.
Necessary technical background to linear stochastic dynamic systems will be given.
The theoretical basis for VAR based methods, integrated and cointegrated variables and issues concerning identification and exogeneity are also explained.
Methods for estimation and interpretation of econometric models that may include non-stationary variables make out one central theme. Both single equation methods and the 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.
The course discusses strategies of model specification and explains several methods for model evaluation. Specification and evaluation are also related to the modelling purpose: estimation, policy analysis or forecasting.
Dynamic models with forward looking expectations play a central role in modern macroeconomics. Such specifications appear in the DSGE models used by most central banks. The solution, specification, estimation and econometric assessment of such model are also covered by the course.
The lectures will refer to economic examples and make use of data related to, for example, wage-price dynamics, stock-flow-dynamics (investment vs. capital, purchase flows vs. stocks of consumer durables), and interest rates.
In the seminars the students will have the opportunity to apply the methods and techniques to real world phenomena. For this purpose, relevant software will be used.
You should know
- assumptions and properties of the statistical models and distributions used in modern dynamic econometrics, with special emphasis on single equation dynamic models, the Vector Autoregressive model (VAR) and dynamic systems of equations, as well as the relationship between these classes of models
- how to test hypotheses about cointegration relationships in a system and how to determine empirically the number of such relationships
- how to estimate and do inference in cointegrated VAR’s
- the different concepts of exogeneity, how these concepts can be tested, and how they are related to the different purposes of the modeling exercise: Hypothesis testing, forecasting and policy evaluation
- how to work with model specification and they should have a practical knowledge of the different methods and techniques for model evaluation
- how models with forward-looking variables can be estimated and tested
- the sources of failures and successes in economic forecasting, and should be given an orientation of the main models used in economic forecasting
You should be able to
- formulate dynamic econometric models theoretically
- test the assumptions of dynamic econometric models using modern software
- apply dynamic econometric model, for policy modeling and for forecasting
- be able to read and understand project reports and journal articles that make use of the concepts and methods that are introduced in the course
- be able to make use of the course content in your own academic work, for example in analyses of time series data that are part of the master’s- or PhD thesis
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.
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.
Students who are admitted to study programmes or individual courses at UiO must each semester register which courses and exams they wish to sign up for in StudentWeb.
International applicants, if you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures for international applicants.
Formal prerequisite knowledge
Recommended previous knowledge
10 credits against ECON5125/ ECON9125 - Time series econometrics for non-stationary variables
10 credits against ECON9101 - Advanced Econometrics - Time Series
3 credits against ECON5102/9102 - Advanced Econometrics - Microeconometrics
3 credits against ECON5103/9103 - Advanced Econometrics - Panel Data
Access to teaching
A student who has completed compulsory instruction and coursework and has had these approved, is not entitled to repeat that instruction and coursework. A student who has been admitted to a course, but who has not completed compulsory instruction and coursework or had these approved, is entitled to repeat that instruction and coursework, depending on available capacity.
The students will be evaluated on the basis of a portfolio assessment.
Language of examination
You may submit your response in Norwegian, Swedish, Danish or English. If you would prefer to have the exam text in English, you may apply to the course administrators.
Students on master's level are awarded on a descending scale using alphabetic grades from A to E for passes and F for fail. Students who would like to have the course approved as a part of our phd-program, must obtain the grade C or better. Students on phd-level are awarded either a passing or failing grade. The pass/fail scale is applied as a separate scale with only two possible results.
Explanations and appeals
It is recommended to request an explanation of your grade before you decide to appeal.
The deadline to request an explanation is one week after the grade is published. For oral and practical examinations, the deadline is immediately after you have received your grade.
The explanation should normally be given within two weeks after you have asked for it. The examiner decides whether the explanation is to be given in writing or verbally.
Resit an examination
Special examination arrangements
Application form, deadline and requirements for special examination arrangements.
The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.
This course prepares for the Ph.D. program. It provides a head start for last-year master students who intend to continue with a Ph.D.