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Introduction

This course is of great importance for your master thesis. It will provide you with the knowledge and skills to test empirical predictions of theories from finance or economics and critically assess the methodology employed in research papers. Additionally, the library session will familiarize you with information search strategies.

In class, we will introduce econometric concepts and discuss the intuition behind them. You will learn how to implement these econometric techniques in R and then you need to apply them to real-world data, in an assignment. 

Course content

Regression analysis

• Classical linear regression model (CLRM)
• CLRM assumptions and the diagnostic tests
• Panel regressions

Time series modeling

• Univariate time series analysis
  • Moving average (MA) processes
  • Autoregressive (AR) processes
  • ARMA processes
  • Box-Jenkins methodology
  • Forecasting in econometrics
• Multivariate time series analysis
  • Vector autoregressive (VAR) models
  • Granger causality tests
  • Impulse responses and variance decompositions

Cointegration and volatility modeling

• Cointegration: Modelling long-run financial behavior
  • Stationarity and unit root testing
  • Cointegration
  • Error correction models
  • Testing for cointegration
• Modeling volatility: GARCH models
  • Models for volatility
  • Autoregressive conditionally heteroscedastic (ARCH) models
  • Generalized ARCH (GARCH) models
  • Maximum likelihood estimation

Information search strategies

• Search strategies
• Literature review articles
• Evaluation of sources