Special Topics in Applied Econometrics

Lecturer

The lecturer is Maoliang Ye, Assistant Professor at Xiamen University (P.R. China).

Contact

Maoliang Ye

Xiamen University
361005 Xiamen, Fujian Province
P.R. China
maoliang.ye@xmu.edu.cn

Pre-requisites

Students must have followed an introductory course to Econometrics, providing them a good knowledge of the linear econometric models.

Students must be able to use STATA, a standard econometric software package.

Lecture topics

Chapter 1 | The evaluation problem and random control trial (RTC)

  • The evaluation problem and its challenge (selection bias)
  • Econometric method for randomized control trial under perfect compliance
  • Econometric method for randomized control trial under imperfect compliance
  • Design and implementation of field experiments

Chapter 2 | ​Applications of instrumental variable (IV) method

  • Review of IV methods
  • Practical issues in using IV
  • Example papers
  • Practice

Chapter 3 | ​Difference-in-difference (DID)

  • DID methods and its different forms
  • Practical issues in using DID
  • Example papers
  • Practice

Chapter 4 | ​Panel data

  • Formulation: Fixed effects, random effects, within and between transformations
  • FGLS and ML estimation
  • Endogeneity problems
  • Introduction to dynamic models

Chapter 5 | Regression discontinuity design

  • Regression discontinuity design
  • Practical issues
  • Example papers
  • Practice

Course contents

The course focusses on the econometric methods for causal inference and can also be used in international economics:

  • The evaluation problem and random control trial
  • Application of the instrumental variable method
  • Difference-in-difference
  • Panel data
  • Regression discontinuity design

It also deals with example papers and applications based upon specific datasets.

Core reading

  • Joshua D. Angrist and Jörn-Steffen Pischke (2009): Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press
  • W.H. Greene, Econometric Analysis, 7th edition

Learning outcomes

Upon successful completion of the course, students should be able:

  • To formulate the model and to choose between fixed and random effects
  • To estimate the model by FLS

For panel data

  • To formulate the latent model and its link to the observable model
  • To estimate the standard LDV models by maximum likelihood: logit, probit, tobit, nested logit
  • To interprete the estimation results

For limited dependent variables

  • To estimate these models
  • To apply them to gravity models used in international trade theory

For counting and Pseudo-Poisson models

Organisation

The course is spread over 2 weeks and 10 classes: 8 classes for teaching and 2 classes for exercises and Q&A.

Assessment

Students are evaluated through problems sets and a written exam.