Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

Students learn
- statistical modeling
- to understand how to analyze time series data and make forecasts in economics and business
- to design statistical research.

Credits: 6

Schedule: 01.03.2022 - 14.04.2022

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Tomi Seppälä, Hannu Kahra

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content
  • valid for whole curriculum period:

    Topics in linear models and time series analysis: special estimation methods of regression models, ARMA and ARIMA models, forecasting, stationarity, integrated series, cointegration, ARCH and GARCH models, multivariate models, panel data

Assessment Methods and Criteria
  • valid for whole curriculum period:

    NOTE! Students must pass a preliminary assignment in order to be able to attend the course. More information will be given on the MyCourses page of the course closer to the begin of the registration.

    exam
    assignments

Workload
  • valid for whole curriculum period:

    Contact teaching
    Independent work 
    Exam 

DETAILS

Substitutes for Courses
Prerequisites

FURTHER INFORMATION

Further Information
  • valid for whole curriculum period:

    Teaching Language : English

    Teaching Period : 2022-2023 No teaching
    2023-2024 No teaching