Course description

Research Methodology - Economics

Introduction

This course is a graduate level introduction to Econometrics.

Course content

I Data Management

  • Traditional data (time series and panel data)
  • Big data
  • Data reduction
  • Web scraping

II Time series – Stationary and non-stationary univariate time series

  • White noise, moving average, autoregressive models
  • Forecasting
  • Deterministic and stochastic trends, unit roots, structural change
  • Applications

III Vector autoregression (VAR) methodology

  • Structural VARs specification and estimation
  • Identification, impulse responses, variance decomposition
  • Spurious regression and log run economic relationships
  • Applications

IV Panel data analysis

  • Difference in difference methodology
  • Fixed effects
  • Applications

V Introduction to information search strategies (3 hours in the pc-lab)

  • Acquaintance with methods for information, harvesting and search techniques
  • Know what a critical literature review is and how this type of articles may be searched for and used
  • Critical evaluation of sources

VI Setting up an econometric project

  • Research ethics, data handling, specification, modeling, policy analysis

Learning outcome knowledge

Students will learn to use statistical methods for estimating economic relationship, testing economic theories, and using estimated models to analyze the effect of policy intervention for the public and the private sector.

Exam organisation

  • Presentation: 20%
  • Written assignment: 10%
  • Written exam: 70%