ITA | ENG
B004651 - DEMOGRAPHIC MODELS
Main information
Teaching Language
Course Content
Suggested readings
Learning Objectives
Prerequisites
Teaching Methods
Type of Assessment
Course program
Academic Year 2016-17
Course year
First year - Second Semester
Belonging Department
Statistics, IT and its applications "G. Parenti" (DISIA)
Course Type
Single education field course
Scientific Area
SECS-S/04 - DEMOGRAPHY
Credits
9
Teaching Hours
72
Teaching Term
27/02/2017 ⇒ 01/06/2017
Attendance required
No
Type of Evaluation
Final Grade
Course Content
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Course program
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Lectureship
Teaching Language
ENGLISH
Course Content
This course is an introduction to theory and applications of event-history analysis. Duration data is commonly used to address many research questions in demography, social sciences, and epidemiology.
Suggested readings (Search our library's catalogue)
Blossfeld, H., K. Golsch, and G. Rohwer. 2007. Event History Analysis with Stata. Mahwah, NJ: Lawrence Erlbaum.
Learning Objectives
This course covers univariate and multivariate (regression) methods for analysis of duration (event-history) data, including their recent developments. Students also learn data management skills that are specific to conducting event-history analysis in Stata.
Prerequisites
Statistical Inference
Teaching Methods
Throughout the course students will apply event-history techniques to own research projects.
Type of Assessment
Written exam concerning all material of the exam. It icludes both exercises in STATA and questions on theory.
Course program
Introduction (Basic concepts and definitions, Event history data, censoring and truncation, discrete vs. continuous time); Event history data (Coding and data preparation, Life tables, Kaplan-Meier, related estimators, Stata applications, time-constant and time-varying variables); Non-parametric models (Exponential and piece-wise constant models); Modelling-related issues (Interactions and combinations of variables; model choice and goodness of fit); Parametric models (Weibull, Gompertz, Log-Logistic, Log-Normal); Cox model (Estimation, interpretation of parameters and model diagnostics, PH assumption); Competing risk models (Data preparation, estimation and interpretation); Advanced topics (Discrete time models, frailty models – unobserved heterogeneity, multiprocess and multilevel extensions of event history models)