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​​Microeconometrics

Dynamic and Non-Linear Panel Data for Policy Evaluation

Advanced dynamic and discrete choice panel data models: theory and practical application with STATA.

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17.5h (5 days)
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€1,470
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Face-to-face
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English
Next edition: July 12-16, 2027
Early Bird deadline: April 15, 2027
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​​Microeconometrics
Dynamic and Non-Linear Panel Data for Policy Evaluation
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Course overview

This course provides up-to-date coverage of dynamic panel data models, discrete choice (linear and non-linear) panel data models, as well as censored panel data models and the estimation of dynamic panel data models subject to selection. Apart from a review of relevant theory, the focus of the course is on the practical application of these models to various data contexts: small T – large N, unbalanced panels, rotating panels and pseudo panels constructed from cohort data. All classes have applications to policy evaluation

Practical sessions provide the audience with a guide on how to proceed with these models, particularly on how to phrase the restrictions and contrasts to adequately specify the model to be estimated. All the practical sessions are conducted in STATA.

Faculty

Who is this course for?

This course has been purposely designed for:

  • Master’s and PhD students with a solid quantitative background, as well as professionals from public and private institutions who want to strengthen their skills in advanced panel data methods for policy evaluation.

Learning outcomes

By the end of the course, participants will have:

  • Used models for various data types, including small T – large N, unbalanced panels, rotating panels, and pseudo panels
  • Policy evaluation with panel data models
  • 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
  • Deep knowledge of the use of the linear probability model for policy evaluation

Key topics for Dynamic and Non-linear Panel Data Models for Policy Evaluation course

Find out what you will cover during this course.

Course outline

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  • Dynamic Panel Data and extensions 
  • Sample selection panel data models; Application to policy evaluation
  • Censored Panel Data Models for policy evaluation
  • Linear static and dynamic discrete choice panel data models for policy evaluation
  • Non-linear static and dynamic discrete choice panel data models for policy evaluation

List of References

Below is a list of texts that may help you prepare for the course.

Recommended Texts

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  • 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

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  • STATA will be used during the course. Participants will receive a temporary personal license, provided by BSE, a few days before Summer School begins
  • Participants must bring their own portable laptop to participate fully in this course

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.

Next edition: July 12-16, 2027
Early Bird deadline: April 15, 2027

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 for Policy Evaluation Course

  • Some background in Econometrics is strongly recommended

Schedule

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

Time
12
mon
13
tue
14
wed
15
thu
16
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 who attend more than 80% of the course will receive a Certificate of Attendance, free of charge.

Course
Dynamic and Non-linear Panel Data Models for Policy Evaluation
Beyond Average Effects: Quantile Regression, Distributional Analysis and Inequality
Difference-in-Differences with Panel Data
Dynamic Structural Models for Policy Evaluation
Quantitative Methods for Public Policy Evaluation
Modality
Face-to-face
Face-to-face
Face-to-face
Face-to-face
Face-to-face
Total Hours
17.5
17.5
17.5
17.5
17.5
ECTS
1
1
1
1
1
Regular Fee
€1,470
€1,470
€1,470
€1,470
€1,470
Reduced Fee*
€840
€840
€840
€840
€840

* Reduced Fee applies for PhD or Master’s students, Alumni of BSE Master’s programs, and participants who are unemployed.

Multiple discounts are available.

For more information on discounts for participants funding their own course, please click here.

For more information on discounts for companies and institutions enrolling their employees, please click here.

Full discount conditions are available here.

FAQ

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

Can I see the full Summer School calendar?

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You can view the full Summer School calendar here.

Is accommodation included in the course fee?

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

Are the sessions recorded?

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Sessions will NOT be recorded; however, the materials provided by the professor will be available for a month after the course has finished.

How much does each Summer School course cost?

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

Are there any discounts available?

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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.

Can I take more than one course?

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Yes! you can combine any of the Summer School courses (schedule permitting). See the full course calendar.

Cancelation and Refund Policy

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Please consult BSE Summer School policies for more information.

Are there any evening activities during the course?

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Yes, a social dinner is held once a week for all participants, it is free to attend.

Contact our Admissions Team

Mix and match your summer courses!

Remember that you can combine this program with courses in any of the other BSE Summer School programs (schedule permitting). Maximise your learning this summer and take advantage of our multiple-course discount.

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