Chapter 11: Models for Panel Data

Chapter 12: Estimation Methods

Chapter 14: Maximum Likelihood

Chapter 15: Simulation Based Estimation and Inference

Chapter 17: Models for Discrete Data

Chapter 25: Models for Unordered and Ordered Discrete Choices

**Topics **

**1. Linear Regression 6. Censoring and Two Part Models **

Descriptive tools Tobit model

Specification analysis Truncation and censoring

Estimation and inference Hurdle models and zero inflation

**2. Endogeneity 7. Nonlinear Panel Data Models **

IV and 2SLS estimators Binary choice

Control functions Clustering

Sample selection Random effects, fixed effects

Propensity score matching Dynamic models

**3. Panel Data 8. Hetrogeneity and Mixed Models **

Pooled data, clustering Latent class modelling

Difference in differences Random parameters

Fixed effects, FEVD, random effects

Mundlak specification

**4. Binary Choice Models 9. Multinomial choice models **

Nonlinear models and means Multinomial logit

Binary choice Specification and estimation/inference

Partial effects and interactions Extensions, nested logit, heterogeneity

Estimation and inference Random parameter models

Endogenous RHS variables Stated preference data

**5. Ordered Choices and Count Data 10. Stochastic Frontiers **

Ordered choice models Normal-half normal model

Fits, estimation, inference Estimating inefficiency

Models for count data Panel data models

Overdispersion, negative binomial

The course outline can also be downloaded HERE

**The schedule for the course will be as follows;**

9.00 - 10.30 Session 1

10.30 - 10.50 Break

10.50 - 12.30 Session 2

12.30 - 13.30 Lunch

13.30 - 15.00 Session 3

15.00 - 15.20 Break

15.20 - 17.00 (or so) Lab