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- Code:
- ECOM-G315
- Field:
- Econometrics
- Target:
- Master’s students
- Organiser:
- University of Helsinki - Economics
- Instructor:
- Henri Nyberg
- Period:
- Period 2
- Format:
- Participation in teaching
- Method:
- Distance teaching
- Enrollment:
In case of conflicting information consider the Sisu/Course/Moodle pages the primary source of information.
Aalto, Hanken and UH economics students can enroll through their home university’s SISU. Further instructions are available on the How to enroll? page, also for students from other universities.
If you would like to count the credits towards your degree, please check your curriculum or contact your supervisor or student services for guidance.
- To access the Moodle course area, use all the features and participate in the activities (assignments, discussions), you must have successfully registered for the course in Sisu and logged in with your UH user ID.
- For more information on how to activate your UH user ID and register for a Moodle course area, click here.
Content
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. This course concentrates on capturing this dependence by means of linear univariate and multivariate time series models.
After discussing the general properties of time series, univariate linear autoregressive moving average (ARMA) processes are introduced. Next, autoregressive conditional heteroskedasticity (ARCH) models designed to capture time-varying volatility of economic time series are considered. For modelling the joint dynamics of economic time series, the linear vector autoregression is introduced. To capture unit root type nonstationarity common in economic time series, univariate and multivariate unit root processes are considered. The latter facilitate capturing long-run equilibrium (cointegration) relations. Statistical inference in linear regression models for time series data involving variables with unit roots is also discussed.
Throughout the course, the emphasis is on the practical aspects of econometric modelling and forecasting 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.
Learning outcomes
After the course, the student should
- knows the basic properties of linear univariate and multivariate time series models and the related methods introduced,
- understands the concepts of stationarity, unit root and cointegration, and knows how to deal with unit root type nonstationarity in empirical work,
- is familiar with autoregressive conditional heteroskedasticity models designed to capture time-varying volatility,
- is able to critically follow empirical research that employs the methods considered,
- is able to apply the methods covered in empirical research.