ECN430 Empirical Macroeconomics and Finance (E)

• Topics

  In empirical macroeconomics and finance we are investigating dynamic relationships between macroeconomic and financial variables, mostly in dynamic settings.

  This course will discuss methods for inference and forecasting in dynamic models in macroeconomics and finance, including:

  • time series;
  • trend and cycles;
  • regression with panel data;
  • identification and instrumental variables;
  • estimation and forecasting in dynamic models in macroeconomics and finance;
  • estimation and evaluation of structural economic models;
  • volatility clustering and conditional heteroscedasticity.

  The course will also discuss quantitative methods for characterizing and solving structural models where analytical solutions are difficult to obtain, including:

  • formulation of the problem;
  • finding numerical solution;
  • evaluating how the solution depends on parameter assumptions.

• Learning outcome

  This course presents empirical and quantitative methods and discusses applications in macroeconomics and finance. The course gives students a solid basis for practical empirical and quantitative analysis in macroeconomics and finance. The course is especially suitable for students who write on an empirical master thesis in macroeconomics and finance.

  Upon completion of the course, the student can…

  Knowledge

  • recognize practical empirical and quantitative methods in modern macroeconomics and finance.
  • apply their econometrics knowledge both to analyze and write about quantitative problems.

  Skills

  • estimate and evaluate econometric models and analyze empirical questions in macroeconomic and finance.
  • use software to handle data and to do econometric analyses.
  • use software to solve numerical and quantitative problems.

  General competence

  • formulate and answer a research question in macroeconomics and finance.
  • write a term-paper where econometrics and/or quantitative methods are used.
  • presentation of research ideas and the content of their own term-paper.

• Teaching

  The teaching consists of plenary lectures and practical assignments/lab sessions.

• Recommended prerequisites

  Basic knowledge in econometrics/empirical methods is recommended, including multiple regression analysis, testing and inference, heteroskedasticity and serial correlation, instrumental variables estimation.

• Required prerequisites

  None.

• Compulsory Activity

  None.

• Assessment

  The final grade will be based on two practical assignments (40%) and portfolio consisting of term paper and presentation and discussion of term paper (60%). The practical assignments, term paper and presentation must be completed in groups of 2-3 students.

  The topic of the term paper should be decided by the beginning of February, and handed in by the end of March. The assignments are posted throughout the semester (Jan - April).

  The practical assignments and term paper should be written in English.

  The practical assignments and portfolio must be submitted in the same semester.

• Grading Scale

  A - F.

• Computer tools

  The course will focus on R and Python/Jupyter; however, alternative software packages may also be used.

• Literature

  Selected chapters from various textbooks and scientific empirical papers. A full reading list will be announced at the beginning of the course.