Prerequisites: Introductory econometrics, calculus and matrix algebra
Dr Marcia Schafgans
Dr Tatiana Komarova
This course will present an advanced treatment of econometric principles for cross-sectional, panel and time-series data sets. While concentrating on linear models, some non-linear cases will also be discussed, notably limited dependent variable models and generalised methods of moments. The course will focus on modern econometric techniques, addressing both technical derivations and practical applications. Applications in the areas of microeconomics, macroeconomics and finance will be considered.
The topics covered will include.
Module 1: Main Regression. Topics to include: Principles of Estimation (Ordinary Least Squares, Generalized Least Squares and Maximum Likelihood Estimation with Micro-Econometric applications); Principles of Testing (t- and F-test; Wald, Likelihood Ratio, Lagrange Multiplier Testing Principles). Time Series: Basic Time Series Processes; Stationarity and Nonstationarity - Unit roots and Cointegration.
Module 2: Estimation Methodology. Topics to include: Endogeneity in linear regression models; Instruments; 2SLS estimator and Generalized IV estimator; Simultaneous equations. Motivation, definition and asymptotic properties of GMM estimator; Efficient GMM estimation; Over-identifying restrictions. Introduction to Panel Data Models: Fixed effect and random effect models. Arellano-Bond estimator in dynamic panel data models. Introduction to Quantile estimation.
Though no single textbook covers all methods and applications to be discussed in the course, we will recommend the following textbooks primarily for reference or review:
M. Verbeek, Modern Econometrics, (3rd edition), Wiley (2008).
W.H. Greene, Econometric Analysis, (7th edition), Pearson Prentice Hall (2011).
Lectures: 36 hours Classes: 18 hours (1.5 hours each)
Assessment: Two written examinations