Panel Data Analysis (Econometrics II)

Course Description

This is an intermediate level PhD course in the area of Applied Econometrics dealing with Panel Data The range of topics covered in the course will span a large part of econometrics generally though we are particularly interested in those techniques as they are adapted to the analysis of panel or longitudinal data sets Topics to be studied include specification estimation and inference in the context of models that include individual firm person etc effects We will begin with a development of the standard linear regression model then extend it to panel data settings involving fixed and random effects The asymptotic distribution theory necessary for analysis of generalized linear and nonlinear models will be reviewed or developed as we proceed We will then turn to instrumental variables maximum likelihood generalized method of moments GMM and two step estimation methods The linear model will be extended to dynamic models and recently developed GMM and instrumental variables techniques The classical methods of maximum likelihood and GMM and Bayesian methods expecially MCMC techniques are applied to models with individual effects The last third of the course will focus on nonlinear models Theoretical developments will focus on heterogeneity in models including random parameter variation latent class finite mixture and mixed and hierarchical models We will also visit the theory for techniques for optimization in the setting of nonlinear models We will consider numerous applications from the literature including static and dynamic regression models heterogeneous parameters models Fama Macbeth random parameter variation and specific nonlinear models such as binary and multinomial choice and models for count data.

Code

INTA-GB 9912

Credits

3

Subject

Interarea

Programs That Include This Course

• Doctoral Accounting (PhD)
• Doctoral Information Systems (PhD)
• Doctoral Management and Organizational Behavior (PhD)