Time Series Econometrics
Course, Master's level, 2ST111
Autumn 2023 Autumn 2023, Uppsala, 50%, On-campus, English
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 28 August 2023–1 November 2023
- Language of instruction
- English
- Entry requirements
-
120 credits including 90 credits in statistics, or 120 credits including 60 credits in statistics and 30 credits in mathematics and/or computer science.
- Selection
-
Higher education credits (maximum 285 credits)
- Fees
-
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
- First tuition fee instalment: SEK 12,500
- Total tuition fee: SEK 12,500
- Application deadline
- 17 April 2023
- Application code
- UU-26619
Admitted or on the waiting list?
- Registration period
- 27 July 2023–27 August 2023
- Information on registration from the department
Autumn 2024 Autumn 2024, Uppsala, 50%, On-campus, English
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 2 September 2024–5 November 2024
- Language of instruction
- English
- Entry requirements
-
120 credits including 90 credits in statistics, or 120 credits including 60 credits in statistics and 30 credits in mathematics and/or computer science.
- Selection
-
Higher education credits (maximum 285 credits)
- Fees
-
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
- First tuition fee instalment: SEK 12,500
- Total tuition fee: SEK 12,500
- Application deadline
- 15 April 2024
- Application code
- UU-26619
Admitted or on the waiting list?
- Registration period
- 25 July 2024–1 September 2024
- Information on registration from the department
About the course
The course is the first course in a time series analysis focusing on stochastic processes in discrete time. The course covers the Box-Jenkins approach to ARIMA models, that is Identification, Estimation, Evaluation and Forecasting. Fundamental concepts such as stationarity, random walks, seasonality, and co-integration will be covered.