Target group 
Master’s Programme in Economics (General Track). Open to all. 
Timing 
First autumn term, after completing Econometrics 1. 
Learning outcomes 
After the course, the student should
 Be familiar with the differences between cross sectional and time series data in economic research
 Know the basics of linear time series models and the related econometric methods of estimation, inference and forecasting
 Understand the concepts of stationarity, unit root and cointegration, and know the basic methods of dealing with unit root type nonstationarity
 Be able to critically assess empirical analyses based on time series data
 Be able to apply the models and methods covered in the course in empirical research

Completion methods 
The course consists of lectures (24 hours) and exercises either in separate sessions or integrated into the lectures. The lectures and exercise sessions are not mandatory. There is a written final exam and four internet quizzes based on the homework assignments. The homework assignments consist of analytical and empirical exercises. The former familiarise the student with the theory and calculations typically required in the practical implementation of the methods, while the latter teach skills of undertaking an empirical research project, including data handling, programming and interpreting results. 
Prerequisites 
The course builds upon the contents of Econometrics 1. Hence, the student is assumed to be familiar with the linear regression model and the principles of statistical inference to the extent covered in that course. Familiarity with matrices is also assumed. 
Recommended optional studies 
Knowledge of R (or some other matrix programming language) is useful, although the basics needed for the practical implementation of the methods can be acquired during the course 
Contents 
The course provides an introduction to the econometric modelling of time series data that much of empirical economic research is based on, especially in the fields of macroeconomics and financial economics. In addition, time series methods are widely applied in practical tasks, such as economic forecasting and financial risk management. The dependence of observations on time is a feature that distinguishes time series from cross sectional data and calls for appropriate econometric methods. In this course, we concentrate on capturing this dependence by means of linear univariate and multivariate time series models, as well as on the stationarity properties of time series and their effect on statistical inference. After discussing the general properties of time series, we introduce univariate linear stationary autoregressive moving average (ARMA) processes. Because of the ubiquity of unit root type nonstationarity in economic time series, extensive time is devoted to the properties and detection of unit root processes. For modelling the joint dynamics of economic time series, we next introduce the stationary vector autoregression, and subsequently cover its extension to the unit root case, which facilitates capturing longrun equilibrium cointegration relations among economic variables. Finally, we deal with statistical inference in linear regression models for time series data. Throughout the course, the emphasis is on the practical aspects of econometric modelling instead of the foundations of statistical inference. The models and methods are illustrated by means of Monte Carlo simulations and empirical applications to macroeconomic and financial data. 
Study materials and literature 
In addition to the lecture slides, selected parts of Chapters 8–9 as well as Sections 4.10–4.11 of the textbook by Verbeek (A Guide to Modern Econometrics, 4th edition; the 2nd and 3rd editions can be used as well) as well as the manuals of the R packages covered in the course are recommended. 
Activities and teaching methods in support of learning 
All material related to the course is delivered through the Moodle area of the course, which also contains a discussion forum where students can discuss issues related to the course with each other and the teacher. During the lectures, the students can post spontaneous comments and questions in the Presemo room for the course. The lectures involve a number of activating questions, also answered through Presemo. Each homework assignment involves multiplechoice questions that the students answer through Moodle before the exercise sessions related to the homework assignment. 
Assessment practices and criteria 
The grade on a scale from 0 (fail) to 5 is based on the sum of points earned in the final exam and the quizzes based on the homework assignments. The maximum number of points is 100, of which 60 come from the final exam and 40 can be earned in the quizzes based on the homework assignments. To pass the course, the student must earn at least 50 points in total and at least 30 points from the final exam. 