Time Series Analysis and Prediction

ECO403 Time Series Analysis and Prediction

  • Topics

    Topics

    Overview of time series methods in finance, energy market, business administration and economics

    • Linear models (ARIMA)
    • Forecasting
    • Volatility models (GARCH etc.)
    • Other time series models and methods (random walk, cointegration, etc)
    • Other topics on times series can be treated if time allows and according to the interests and needs of the participants. Possible topics are e.g. multivariate ARIMA models, non-linear models, non-parametric models, state-space models and Bayes methodology in time series analysis.

  • Learning outcome

    Learning outcome

    Economic decisions are often based on the analysis of time series data. The purpose could be to investigate historical development, to forecast future development, to test an economic theory, to price a financial asset, to allocate a portfolio, to explain effects of an intervention of or changes in a policy variable or to control a dynamic system. This course gives an introduction in the statistical modelling and prediction of economic and financial time series.

    After completing the course the students will be able to

    • solve difference equations
    • understand the central ideas of time series analysis and forecasting
    • implement the most common techniques to real-world forecasting problems
    • use time series analysis to test economic theory empirically
    • model financial volatility
    • model non-stationary time series variables that evolves simultaously over time
    • know the structure and requirements for a master thesis
    • be able to develop a research question for a master thesis
    • be able to choose and apply an appropriate scientific method for analysing the research question
    • be able to interpret empirical material in business and economics
    • be able to analyse and deal critically with empirical works in major scientific journals in business and economics
    • understand the ethical issues in collection and interpretation of data

  • Teaching

    Teaching

    Lectures and computer lab.

  • Required prerequisites

    Required prerequisites

    Basic skills in statistical inference.

  • Requirements for course approval

    Requirements for course approval

    The participants shall work with compulsory assignments during the course. These include theory as well as data analysis. Course approval (the right to attend the written final exam) is given on the basis of these assignments.

    Note: There might be compulsory activities in the course prior to the registration deadline.

  • Assessment

    Assessment

    Written 4 hours exam. The grade of the course is based on the written exam.

  • Grading Scale

    Grading Scale

    Grading scale A - F.

  • Computer tools

    Computer tools

    The course contains use of the statistical packages R

  • Semester

    Semester

    Spring.

  • Literature

    Literature

    Enders: Applied econometric time series 3ed. Wiley 2010.

    Cowpertwait and Metcalfe: Introductory time series with R. Springer 2009.

    Recommended readings:

    Mills: The econometric modelling of financial time series, 2 ed. Cambridge 1999.

    Tsay: Analysis of financial time series. Wiley 2002.

    Hamilton: Time series analysis, Princeton University Press 1994

Overview

ECTS Credits
7.5
Teaching language
English
Semester
Spring

Course responsible

Jonas Andersson, Department of Business and Management Science