Discrete Choice Models
Course Description
This course cover methods for empirical models that have dependent variables that are not continuous. These models include binary response models; ordered response models, multinomial response models, models for censored and truncated data, count data, and models for individual heterogeneity.
Goals
Upon completion of the course, students should be able to:
Requirements
This course cover methods for empirical models that have dependent variables that are not continuous. These models include binary response models; ordered response models, multinomial response models, models for censored and truncated data, count data, and models for individual heterogeneity.
Software
We will use R and Stata for computational exercises, with more emphasis on Stata. R can be downloaded using this link . Another important program that might be useful is Rstudio. This is an integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management. Rstudio can be downloaded at here. Students are welcome to use other software packages, but I will provide support only for R and Stata. Be prepared to devote a good amount of time programming, which is essential for becoming proficient in the methods covered in this course.
Required Text:
The book texts required are the following:
Slides:
This course cover methods for empirical models that have dependent variables that are not continuous. These models include binary response models; ordered response models, multinomial response models, models for censored and truncated data, count data, and models for individual heterogeneity.
Goals
Upon completion of the course, students should be able to:
- Select and apply appropriate models for qualitative/discrete choice models.
- Master estimation and interpretation of qualitative/discrete choice models.
- Estimate model with R and Stata.
- Code model for qualitative/discrete choice models.
Requirements
This course cover methods for empirical models that have dependent variables that are not continuous. These models include binary response models; ordered response models, multinomial response models, models for censored and truncated data, count data, and models for individual heterogeneity.
Software
We will use R and Stata for computational exercises, with more emphasis on Stata. R can be downloaded using this link . Another important program that might be useful is Rstudio. This is an integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management. Rstudio can be downloaded at here. Students are welcome to use other software packages, but I will provide support only for R and Stata. Be prepared to devote a good amount of time programming, which is essential for becoming proficient in the methods covered in this course.
Required Text:
The book texts required are the following:
- (L)- Scott Long, J. (1997). Regression Models for Categorical and Limited Dependent Variables. Advanced quantitative techniques in the social sciences, 7.
- (LF)- Long, J. S., & Freese, J. (2006). Regression Models for Categorical Dependent Variables using Stata. Stata press.
- (M)- Maddala, G. S. (1986). Limited-Dependent and Qualitative Variables in Econometrics (No. 3). Cambridge university press.
- (GH)- Greene, W. H., & Hensher, D. A. (2010). Modeling Ordered Cthoices: A primer. Cambridge University Press.
- (WB)- Winkelmann, R., & Boes, S. (2006). Analysis of Microdata. Springer Science & Business Media.
- (CT)- Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: Methods and Applications. Cambridge university press.
- (CTS)- Cameron, A. C., & Trivedi, P. K. (2009). Microeconometrics Using Stata (Vol. 5). College Station, TX: Stata press.
- (T)- Train, K. E. (2009). Discrete Choice Methods with Simulation. Cambridge university press.
- (R)- Ruud, P. A. (2000). An Introduction to Classical Econometric Theory. OUP Catalogue.
Slides: