2013 Training Course - Applied Panel Data Econometrics

Event Date
International Food Policy Research Institute (IFPRI), Dakar, Senegal


The wide availability of panel data presents unique opportunities for researchers. It is essential to understand the intuition and implications of the estimators and tests that are currently available to successfully take advantage of panel data. This course will focus on core panel data techniques, building a strong foundation before moving into more cutting edge methods that have been recently developed. The class will also discuss in detail implementation of panel data estimators and tests using the popular open source software R. The class will take place over five days to ensure maximum coverage of the core topics.

Course Objectives

Participants of this course should leave with the ability to understand the nuances of panel data estimators and the empirical implications that manifest. Further, participants should be able to successfully integrate their data into R and construct appropriate panel data models which they can estimate, conduct inference and rigorously interpret to provide sound policy insights. All methods discussed will be accompanied with corresponding R code, data and documentation to the literature at large making it easy for participants to follow along in the class as well as a check once the class has ended and they are engaged in their own analysis.

Course Outline

  1. Introduction to R using the basic linear regression model (all notes, data and examples will be provided)
  2. Advantages of panel data in applied work
  3. The one-way error component panel data model
    1. Individual Effects
    2. The Incidental Parameters Problem
    3. Fixed Effects Estimation
    4. Random Effects Estimation
    5. Fixed versus Random Effects
    6. The Hausman Test
    7. Applications
  4. The two-way error component panel data model
    1. Time Effects
    2. Fixed Effects Estimation
    3. Time Effects Estimation
    4. Tests of Poolability
    5. Tests for the Presence of Time or Individual Effects
    6. Unbalanced Panel Estimation
    7. Applications
  5. System Estimation with Panel Data
    1. Seemingly Unrelated Regression with Error Components
    2. System Estimation
    3. Hausman/Taylor Estimation
    4. Applications
  6. Dynamic Panel Estimation
    1. Arellano and Bond Estimator
    2. Arellano and Bover Estimator
    3. Ahn and Schmidt Estimator
    4. System GMM
    5. Applications
  7. Quasi-Experimental Analysis with Panel Data
    1. What is a Quasi-Experiment?
    2. The Difference-in-Difference Estimator
    3. Threats to Validity
    4. Applications
  8. Advanced Panel Data Estimators
    1. Estimation with Spatial Weighting
    2. Heckman Selection in Panel Estimation
    3. Logit and Probit Estimation
    4. Count Data Panel Estimation
    5. Censored/Truncated Panel Estimation
    6. Applications


Introduction to R

Lesson 1

Lesson 2

Lesson 3

Lesson 4

Lesson 5

Lesson 6

Lesson 7

Lesson 8

Lesson 9

Lesson 10

Lesson 11


Once selected, course participants will have the opportunity to provide real data applications, one of which will be used as an example during the course.

course requirements


The course level is appropriate for participants with a background in economics, statistics, mathematics, and/or public policy. A strong background in quantitative analysis is required. Basic knowledge of the statistical software R is desirable. A general fluency in the statistical/econometric lingo at the (post-) doctoral level (hopefully in a non-statistics/econometric discipline) is required. More specifically, the Law of Large Numbers and the Central Limit Theorem should be understood.


This course will heavily leverage implementation in R, a powerful statistical software package that is freely available. R possesses the facilities to implement an impressive array of panel data estimators as well as to serve as an interface for data manipulation, making it an ideal choice when discussing the application of panel data estimators. Moreover, R's real strength is that users can readily and easily construct their own estimators and tests so that canned approaches do not need to be relied on, allowing users to stand on their legs when conducting empirical research.


In order to apply for this course, AGRODEP members must complete the following by July 30, 2013:

If you would like to practice using Stata before taking the proficiency test, please review the modules below. Information included covers Stata use for beginners, linear regressions, bivariate regressions, and panel data. You will need to know this information to successfully complete the test.


Christopher F. Parmeter is an Associate Professor in the Economics Department at the School of Business Administration at the University of Miami. His area of expertise is in applied econometrics with special interests in semi- and nonparametric methods, benefit transfers, meta-analysis and efficiency analysis. Dr. Parmeter has coauthored 30 peer reviewed scientific articles in leading econometric and applied economics journals.