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Microeconometrics

Dynamic and Non-linear Panel Data Models

Advanced Dynamic and Discrete Choice Panel Data Models: Theory and Practical Application with STATA

17.5h (5 days)

€775 - €1,375

Face to Face

English

Program date: July 14 - 18, 2025

Early bird deadline: April 15, 2025

Learn more

Dynamic and Non-linear Panel Data Models

Applications for 2025 Summer School programs are now open!

Apply

Summer school

Microeconometrics

Dynamic and Non-linear Panel Data Models

This course covers dynamic, discrete choice, and censored panel data models, focusing on practical applications in various contexts, including small T, large N, unbalanced, rotating, and pseudo panels, along with key theoretical concepts. Practical STATA sessions guide model specification and estimation.

Teaching Faculty

Understand Advanced Panel Data Models

By the end of the course, participants

Used models for various data types, including small T – large N, unbalanced panels, rotating panels, and pseudo panels.
Gained hands-on experience in specifying and estimating models using STATA.
Learned to phrase restrictions and contrasts for accurate model specification.
Analyzed and interpreted results from dynamic and discrete choice panel data models.

Program Syllabus for Dynamic and Non-linear Panel Data Models

Find out what you will cover during this course

Course outline

Review of Linear Panel Data models & Introduction to dynamic panel data models.
Dynamic Panel Data and extensions.
Censored Panel Data Models.
Sample selection Panel Data Models. New developments.
Discrete choice Panel Data Models: static and dynamic.

List of References

Recommended texts

Arellano, M. (1992), “Discrete choice for Panel Data”, Investigaciones Economicas, 2003.
Arellano, M. and O. Bover (1995), “Another look at the instrumental-variable estimation of error components models”, Journal of Econometrics 68, 29-51.
Arellano, M. and R Carrasco (2003), ”Binary Choice models with predetermined variables”, Journal of Econometrics, 115, 155-165.
Arellano, M. and B Honoré (2003), ”Panel Data Models, some recent developments”, Journal of Econometrics, 115, 155-165.
Baltagi, Badi H., Econometric Analysis of Panel Data, 6th Edition, Springer, 2021.
Badi H. Baltagi, Sergi Jiménez-Martín, José M. Labeaga, Majid al Sadoon, Consistent estimation of panel data sample selection models, Econometrics and Statistics, 2023.
Blundell, R. and S. Bond (1998), “Initial conditions and moment restrictions in dynamic panel data models”, Journal of Econometrics 87, 115-143.
Steve Bond, 2002, ”Dynamic panel data models: a guide to microdata methods and practice,” CeMMAP working papers CWP09/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
Collado, MD (1997), ”Estimating dynamic models from time series of independent cross-sections”, Journal of Econometrics, 82, 37–62.
Deaton, A. 1985. Panel data from time series cross-sections. Journal of Econometrics 30, 109-126.
Dustman, C. and Rochina-Barrachina, M. R. (2007) ”Selection Correction in Panel Data Models: An Application to Labour Supply and Wages” Econometrics Journal (2007), volume 10, pp. 263293. doi: 10.1111/j.1368-423X.2007.00208.
Jimenez-Martin, Labeaga and Rochina-Barrachina (2009), ”Comparison of estimators in dynamic panel data sample selection and switching models”, mimeo, presented in the Cambridge PD conference.
Sergi Jiménez-Martín & José María Labeaga, 2016. “Monte Carlo evidence on the estimation of AR(1) panel data sample selection models, “Working Papers 2016-01, FEDEA.
Hansen, L.P. (1982), “Large sample properties og gneralized method of moments estimators”, Econometrica 50, 1029-1054.
Jiménez-Martín, S. (1999), ”Controlling the endogeneity of strike variables in the estimation of wage settlement equations”, Journal of Labor Economics, 17, 587-606.
Jones, A.M. and J.M. Labeaga (2003), “Individual heterogeneity and censoring in panel data estimates of tobacco expenditures”, Journal of Applied Econometrics, 18, 157-177.
Kyriazidou, E. (1997), ”Estimation of a panel data sample selection model”, Econometrica 65, 1335-1355.
Kyriazidou, E. (2001), ”Estimation of dynamic panel data sample selection models”, Review of Economic Studies 68, 543-572.
Labeaga, JM. (2001), “A double-hurdle rational addiction model with heterogeneity: estimating the demand for tobacco”, Journal of Econometrics, 93, 49-72.
Labeaga, JM. (2001), “Efficiency comparisons in dynamic panel data models with limited dependent variables”, WP, UNED, Madrid.
Nickell, S. (1981), “Biases in dynamic models with fixed effects”, Econometrica 49, 1417-1426.
Semykina A. and Jeffrey M. Wooldridge ”Estimation of Dynamic Panel Data Models with Sample Selection”, April 28, 2009.
Semykina, A. and Wooldrigde, J.M. (2005), ”Estimating Panel Data Models in the Presence of Endogeneity and Selection: Theory and Application,” mimeo.
Windmeijer, Frank, 2005. ”A finite sample correction for the variance of linear efficient two-step GMM estimators,” Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May.
Wooldrigde, J.M. ”Selection Corrections for Panel Data under Conditional Mean Independence Assumptions,” Journal of Econometrics, 68 (1995): 115-132.

Software / Hardware

Participants must bring their own portable laptop to participate fully in this course.
STATA.

Why join our Summer School?

All BSE Summer courses are taught to the same high standard as our Master’s programs. Join us to:

1

Network with like-minded peers.

2

Study in vibrant Barcelona.

3

Learn from world-renowned faculty.

Admissions and requirements

Participants to BSE Summer School must check they are eligible to apply.

Program date: July 14 - 18, 2025

Early bird deadline: April 15, 2025

Requirements

Summer School applicants normally demonstrate one or more of the following:

A strong background in Economics or a field closely related to the course topic (Statistics, Law, etc.)
Postgraduate degree or current Master’s/PhD studies related to the course topic
Relevant professional experience

Requirements for Dynamic and Non-linear Panel Data Course

Some background in Econometrics is strongly recommended

Apply now

Schedule

Here is your schedule for this edition of BSE Microeconometrics Dynamic and Non-linear Panel Data Models course.

Time

14

mon

15

tue

16

wed

17

thu

18

fri

11:30 - 13:30

Lecture

16:15 - 17:45

Practical

Credit transfers (ECTS)

To be eligible for credit transfer, students must complete a final project.

Students will deliver a short final project one week after the summer school finishes.

It will consist of completing an assignment that will include the practical and empirical issues worked on in class.

Consult the Summer School Admissions page for more information about this option.

Certificate of Attendance

Participants not interested in credit transfer will instead receive a Certificate of Attendance free of charge. These Participants will not be graded or assessed during the course.

Fees for 2025

Multiple course discounts are available, see more information about available discounts. Fees for courses in other Summer School programs may vary.

Course

Panel Data and Spatial Panels

Econometrics of Cross-section Data with Applications

Dynamic Structural Models for Policy Evaluation

Quantitative Methods for Public Policy Evaluation

Estimation in High-Dimensional Models

Difference-in-Differences with Panel Data

Dynamic and Non-linear Panel Data Models

Modality

Face to Face

Total Hours

17.5

ECTS

1

Regular Fee

1,375€

Reduced Fee*

775€

FAQ’s

Need more information? Check out our FAQ section or contact our Admissions Team.

Is accommodation included in the course fee?

Accommodation is not included in the course fee. Participants are responsible for finding accommodation.

Are the sessions recorded?

Sessions will be recorded and videos will be available for a month once the course has finished.

How much does each Summer School course cost?

Fees for each course may vary. Please consult each course page for accurate information.

Are there any discounts available?

Yes, BSE offers a variety of discounts on its Summer School courses. See more information about available discounts or request a personalized discount quote by email.