Course Overview:
This intensive short professional online course is tailored for academics, PhD students, and professors who seek to enhance their expertise in panel data analysis in economics. The course covers the theoretical foundations, practical implementation, and advanced techniques for analyzing panel data in economic research.
Course Objectives:
Understand the principles and techniques of panel data analysis.
Develop skills to model and estimate economic relationships using panel data.
Apply panel data methods to empirical economic research and policy analysis.
Utilize software tools such as Stata, R, and EViews for panel data analysis.
Target Audience:
Academics in economics and related fields.
PhD students specializing in econometrics, economic research, and data analysis.
Professors and researchers aiming to enhance their analytical skills in panel data methods.
Course Structure: The course is structured into four intensive modules, each featuring lectures, readings, assignments, and practical exercises. Participants will engage with video lectures, interactive coding sessions, and peer discussions.
Week 1: Fundamentals of Panel Data
Introduction to panel data: structure and characteristics.
Advantages of panel data over cross-sectional and time series data.
Week 2: Basic Panel Data Models
Pooled OLS regression.
Fixed effects and random effects models.
Readings:
"Econometric Analysis of Panel Data" by Badi H. Baltagi.
"Panel Data Econometrics" by Mike Tsionas.
Week 3: Dynamic Panel Data Models
Introduction to dynamic panel data models.
The Arellano-Bond estimator and Generalized Method of Moments (GMM).
Week 4: Panel Data with Limited Dependent Variables
Models for binary and count data: logit, probit, and Poisson.
Handling censoring and truncation in panel data.
Readings:
"Analysis of Panel Data" by Cheng Hsiao.
"Microeconometrics: Methods and Applications" by A. Colin Cameron and Pravin K. Trivedi.
Week 5: Dealing with Endogeneity
Instrumental variables and GMM in panel data.
Addressing endogeneity in fixed and random effects models.
Week 6: Nonlinear Panel Data Models
Nonlinear models in panel data analysis.
Semiparametric and nonparametric methods for panel data.
Readings:
"Applied Econometrics Using the SAS System" by Vivek Ajmani.
Selected articles from the Journal of Econometrics and the Review of Economics and Statistics.
Week 7: Policy Analysis Using Panel Data
Applications of panel data methods in policy evaluation.
Case studies on health, education, and labor economics.
Week 8: Real-World Applications and Case Studies
Presentation and discussion of participant projects.
Integrating panel data methods with policy research.
Readings:
Relevant policy papers and case studies from central banks and international organizations.
Selected articles from the American Economic Review and the Quarterly Journal of Economics.
Assignments:
Weekly problem sets and practical exercises.
Mid-term project involving the estimation of a panel data model on an economic issue.
Final Project:
Comprehensive panel data analysis project on a chosen topic.
Presentation and peer review of the final project.
Certification:
Participants who complete all modules, assignments, and the final project will receive a certificate of completion.
Course Delivery:
The course will be delivered through a combination of pre-recorded video lectures, live Q&A sessions, interactive tutorials, and discussion forums.
All course materials, including readings, software guides, and lecture slides, will be available online.
Instructor:
The course will be led by a team of experienced economists and econometricians with extensive expertise in panel data analysis and economic research.
Enrollment:
Participants can enroll through the university’s online learning platform.
Enrollment will be open to individuals with a foundational knowledge in econometrics and data analysis.
By the end of this course, participants will have a robust understanding of panel data analysis techniques and be equipped with the skills to apply these methods to empirical economic research, policy analysis, and forecasting.