Module 2500 in Hohenheim
Advanced Applied Econometrics:
Linear Models and Panel Data

Lecturers

Prof. Dr. Silke Hüttel, silke.huettel@uni-goettingen.de
Prof. Dr. Stefan Hirsch, s.hirsch@uni-hohenheim.de

Aims

At the end of the course, students shall

• understand the basic econometric methods and be able to apply these to real problems,
• understand, apply and interpret theory-based econometric models,
• are able to handle cross section and panel data
• are able to work with the econometrics package R

Skills: Methodological competence, quantitative analysis, conceptual thinking

Contents

• Econometric methods: OLS, GLS, IV, GMM
• Static and dynamic models for panel data
• Mixed effects models
• Empirical problems

Outline

Textbook: Verbeek, M. (2012): A Guide to Modern Econometrics, 4th edition. John Wiley & Sons
relevant chapters/sections or alternative readings given in parentheses

1. Refresher Ordinary Least Squares (OLS) (2.1-2.6; 5.1)
   • The linear regression model
   • The Ordinary Least Squares Estimator (OLSE)
   • Properties of the OLSE (small sample and asymptotic properties)
   • Goodness of fit and hypothesis testing
2. Refresher Generalized Least Squares (GLS) (4.1-4.3; 4.6)
   • Heteroskedasticity (introduction, implications for the estimator, testing, correction)
   • Autocorrelation (introduction, implications for the estimator, testing)
3. Panel Data I: Static Linear Models for Panel Data (10.1-10.3)
   • Introduction to panel data
   • Models, assumptions and estimation
   • Testing
   • Goodness of fit
   • Two-way effects model
4. Mixed Effects (Bates et al. 2015, Gardiner et al. 2009)
   • Random intercepts and random slopes
   • Nested effects versus crossed effects
   • Model selection and estimation issues
5. Endogenous Regressors and Instrumental Variables Estimation (IVE) (5.1-5.4)
   • Review OLS properties and cases where OLS cannot be saved
   • The IV- and GIV-Estimator
   • Testing
   • Control Function Approach
6. Weak Instruments (Stock and Yogo 2002)
   • Problem and detection of weak instruments
   • Testing for overidentifying restrictions
7. Panel Data II: Instrumental Variables (10.4)
   • Panel data and endogenous regressors
   • Hausman-Taylor approach under correlated effects
8. Generalized Method of Moments (GMME) (5.5-5.6)
   • The GMME
   • Illustration
   • Weak Identification
9. Panel Data III: Dynamic Models for Panel Data (10.4-10.5; Bond 2002)
   • Model and assumptions
   • Estimation: Anderson-Hsiao, Arellano-Bond, Blundell-Bond System GMM
   • Testing

Teaching forms	Workload (h)
1 week block seminar with lecture and exercise	40
Exercises	50
Total	90

Examination: Exercises

Grading: In-class and homework assignments, where a minimum score of 50% is required to pass the module.

Credit points: 3 CP

Language: English

Literature

• Core textbook for this course:
  Verbeek, M. (2012): A Guide to Modern Econometrics. John Wiley & Sons; 4th edition.
• Bates, D., Maechler, M., Bolker, B., and S. Walker (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48. https://www.jstatsoft.org/article/view/v067i01/0
• Bond, S.R. (2002): Dynamic panel data models: a guide to micro data methods and practice. Portuguese Economic Journal 1: 141.
• Gardiner, J.C., Z., Luo, Z., and L.A. Roman (2009). Fixed effects, random effects and GEE: What are the differences? Statistics in Medicine 2009; 28:221–239.
• Stock, J. H., J. H. Wright, and M. Yogo (2002). A survey of weak instruments and weak identification in generalized method of moments, Journal of Business and Economic Statistics, 20, 518–29.

Software: R

During the course all exercises can be conducted in R or Stata, while support will be given in R only and our handouts will be in R. Therefore, please familiarize yourself with basics in R if R is new to you. We recommend working at least through the following introductory material:
http://www.r-tutor.com/r-introduction
https://cran.r-project.org/doc/contrib/Torfs+Brauer-Short-R-Intro.pdf