Generalized Linear Models



General Information


This course is predominantly an applied statistical course, with emphasis on statistical theory only when needed. It aims to provide the basic theoretical and operational concepts to the student about the most important Linearized Econometric Models of cross-section data using the Generalized Linear Modeling (GLM) framework. The course will cover estimation and inference principles, the mathematical (algebraic properties) of the Maximum Likelihood method, simple and multiple linearized regression models, tests for functional form and omitted variables, in addition to heteroskedasticity and finite mixture approaches to count data. It will also emphasize the nature of residuals and analyze many of the inspection and tests of goodness-of-fit and influential measures by means of residuals. The empirical part of the course will be based on the R software and data from Berridge and Crouchley (2011) and the theoretical approach from Agresti (2012) and McCullagh and Nelders (1989).  I expect that students read the suggested literature specific to GLM, including the basic texts on mathematical econometrics, probability, and statistical inference, as well participate in the data laboratory classes. At the end of the course I expect students to be able to manipulate cross-section data in R and apply the methods to specific areas of interest in Demography, Geography, Sociology, Economics, and Health Studies.


Tests and Grading:

  • Assignment 1: Student’s active participation in class (20 points) [Download]
  • Assignment 2: Applied use of cross-section data to estimate, interpret and analyze the quality of the model (80 points)

More details: 

Download the complete syllabus here. Download pdf_icon

Data & Scripts



Class 01r_symbol2



Class 1 – Simulating Probability Distributions in R.r_symbol2
Class 1 – Asymptotic Theory in Practice.r_symbol2
Class 1 – GLM in Praticer_symbol2

Writing Materials, Powerpoints & Beamers

Class 1 – Review of Basic Terminology and Random Variables.pdf_icon
Class 1 – Some notes on Maximum Likelihood for Linear Regression.pdf_icon

Compulsory Reading (Textbook)

Weekly Assignments

Teaching Assistant's Material

Video Classes

Introduction to R and R Studio. youtubelogo

Extra Materials



Long, S. Regression Models for Categorical and Limited Dependent Variables (Advanced Quantitative Techniques in the Social Sciences), 1nd Edition, Sage Publications, 1997.

Long, S. and J. Freese. Regression Models for Categorical Dependent Variables Using Stata, 3rd Edition, Stata Press, 2014.



Same Level Reference Books

Kennedy, P. A Guide to Econometrics, Sixth Edition John Wiley & Sons, 2008.

Baum, C. An Introduction to Modern Econometrics Using Stata, Stata Press 2006.

Stock, J.H and M. W. Watson Introduction to Econometrics, 2nd ed., Addison-Wesley, 2006.

Hill, R. Carter, Griffths, William E. and Lim, Guay C. Principles of Econometrics, 3rd ed., John Wiley & Sons, 2008.

Advanced Readings

Goldberger, A. S. A Course in Econometrics 1st US Edition 4th Printing Edition, Harvard University Press, 2000.

Greene,W.H. Econometric Analysis, Seventh Edition, Pearson/Prentice Hall, 2012.

Woodridge, J. Econometric Analysis of Cross Section and Panel Data, 2nd Edition, MIT Press, 2010.

Badi H. Baltagi Econometric Analysis of Panel Data, 4th Edition, Wiley, 2008.

James W. Hardin, Joseph M. Hilbe Generalized Linear Models and Extensions, 2nd Edition Stata Press, 2006.

Colin Cameron, Pravin K. Trivedi Regression Analysis of Count Data, Cambridge University Press, 1998.

John P. Hoffmann Generalized Linear Models: An Applied Approach, Pearson, 2004.

Cheng Hsiao Analysis of Panel Data, 2nd Edition, Cambridge University Press, 2003.