ECN430 Empirical Methods and Applications in Macroeconomics and Finance

• Topics

  In empirical macroeconomics and finance we are investigating dynamic relationships between variables. This course will discuss methods for inference and forecasting in dynamic models in macroeconomics and finance, including:

  • regression with panel data;
  • identification and instrumental variables;
  • estimation and forecasting in dynamic models in macroeconomics and finance;
  • estimation and evaluation of structural econometric models;
  • volatility clustering and conditional heteroscedasticity;
  • non-linear models.

• Learning outcome

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

  Students work on a practical empirical project, and the result will be presented towards the end of the course.

  At the end of the course students should achieve the following goals:

  Knowledge;

  - be familiar with practical econometric 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;

  General competence

  - formulate and answer a research question in macro-econ. and finance

  - write a term-paper where econometrics is used

  - presentation of research ideas and the content of their own term-paper

• Required prerequisites

  From Fall 2015 the following prerequisites will apply:

  Students taking this course are expected to have knowledge about econometrics similar to that covered in one of the courses BUS444, ECN402, ECO403 or FIE401 at NHH. That means: multiple regression analysis, testing and inference, heteroskedasticity and serial correlation, instrumental variables estimation.

• Requirements for course approval

  Mandatory assignments.

• Assessment

  The final grade will be based on  empirical assignments (50%) and  term paper (50%).

• Grading Scale

  Grading scale A - F.

• Computer tools

  STATA or other software (R, Matlab)

• Literature

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