COURSESGENERALIZED LINEAR MODELSGeneral Information Aim: 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 Data & Scripts Data Class 01. Scripts Class 1 – Simulating Probability Distributions in R. Class 1 – Asymptotic Theory in Practice. Class 1 – GLM in Pratice. Writing Materials, Powerpoints & Beamers Class 1 – Review of Basic Terminology and Random Variables. Class 1 – Some notes on Maximum Likelihood for Linear Regression. Compulsory Reading (Textbook) Class 1 – Introduction to Asymptotic Theory. Class 1 – Chapters 1 and 2 (Long, 1997). Weekly Assignments Class 1 – GLM – Binomial Distribution. Teaching Assistant's Material Introduction to R. Video Classes Introduction to R and R Studio. Extra Materials R Markdown Cheat Sheet. Student’s material for Wooldridge’s book. Basic Mathematical Tools. Fundamentals of Probability. Fundamentals of Mathematical Statistics. Summary of Matrix Algebra. The Linear Regression Model in Matrix Form. Statistical Tables. Glossary. References Textbooks 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.