Microeconometrics Summer School

* Laptop required In order to participate in practical sessions, you must bring your own portable computer.

This course provides up-to-date coverage of dynamic panel data models, discrete choice 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. Some background in Econometrics is strongly recommended.

Practical sessions provide the audience with a guide about how to proceed with these models, in particular, in how to phrase the restrictions and contrasts for an adequate specification of the model to be estimated. All the practical sessions are resolved with STATA.

Course Outline

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

Basic References

• 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, B.H. (1995), Econometric Analysis of Panel Data, John Wiley. 4th edition 2008.
• 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.x
• 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
• Jiménez-Martín, S. & J.M. Labeaga & M. al Sadoon, 2020. "Consistent estimation of panel data sample selection models," Working Papers 2020-06, FEDEA.
• 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.
• Majid M. Al-Sadoon & Sergi Jiménez-Martín & José M Labeaga, 2019. "Simple Methods for Consistent Estimation of Dynamic Panel Data Sample Selection Models," Working Papers 1069, Barcelona School of Economics.

About the Instructor

Sergi Jiménez-Martín is Professor of Economics at Universitat Pompeu Fabra and Affiliated Professor at the BSE. He received his PhD from the Universitat Pompeu Fabra in 1994. He is currently Chair of the FEDEA-La Caixa Economía de la Salud y Hábitos de Vida. He is also Associate Editor of Empirical Economics, a member of the Scientific Council of Applied Economic Perspectives and Policy as well as Cuadernos Económicos de ICE.

* Laptop required In order to participate in practical sessions, you must bring your own portable computer.

Overview and Objectives

This course deals with methods and applications of dynamic discrete choice structural models in Economics. The methods that we will discuss are often used by researchers with interests in Labor Economics, Industrial Organization, Quantitative Macro, Development Economics and many other fields in Applied Micro. The course focuses on the estimation of dynamic discrete choice structural models that allow for modelling the behavior of forward looking agents making discrete decisions. These models constitute a useful tool for policy evaluation and an interesting complement to reduced form approaches. In particular, they have the advantage of allowing for ex-ante policy evaluation, and of providing external validity for the inference, subject to the model assumptions. They also have the benefit of providing a close link between economic theory and empirics, and the possibility of making inference about the predictions of a model.

Knowledge of Econometrics (at the level of Wooldridge, 2002) and Dynamic Programming (at the level of Chapters 1-2 of Adda and Cooper, 2003) is strongly recommended.

The course is organized in three blocks. In the first one (3 hours), we will introduce the basic framework, and will review standard estimation techniques based on Maximum Likelihood, which involve solving the dynamic programming problems numerically. The second one (5 hours) will cover an alternative set of estimation methods grouped under the label Conditional Choice Probability estimation methods, which avoid the need of solving for the value functions in estimation. Finally, the third one (2 hours) will review the additional complications involved by dynamic problems that involve game theoretical responses to other agent’s choices. Theory will be complemented with examples from published papers in the literature.

Practical sessions will be devoted to the numerical solution and estimation of these models using STATA (MATA), using simple empirically relevant applications and instructing how to extend these simple applications into fully-fledged rich settings like the ones estimated in the published articles described in the lectures.

Both theory and practical sessions are expected to be interactive. Depending on the class size, breakdown sessions will be considered to foster interaction, especially in the practical sessions.

Course Outline

1. Full solution Maximum Likelihood approaches

a. Introduction
b. Basic framework: conditional independence
c. Motivational example: Rust’s engine replacement model
d. Estimation using full solution techniques
e. Extensions: unobserved heterogeneity and equilibrium

2. Conditional Choice Probability (CCP) estimation

a. Conditional value function representation
b. Finite dependence
c. Estimation methods
d. Aguirregabiria and Mira’s iterative approach
e. Extensions: unobserved heterogeneity and equilibrium

3. An Introduction to Dynamic Discrete Games

References

1. Adda, J. and R. W. Cooper (2003), Dynamic Economics: Quantitative Methods and Applications. The MIT Press.
2. Altug, S. and R. A. Miller (1998), "The Effect of Work Experience on Female Wages and Labor Supply", Review of Economic Studies, 65, 45-85.
3. Aguirregabiria, V. and P. Mira (2002), "Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models", Econometrica, 70, 1519-1543.
4. Aguirregabiria, V. and P. Mira (2007), "Sequential Estimation of Dynamic Discrete Games", Econometrica, 75, 1-53.
5. Aguirregabiria, V. and P. Mira (2010), “Dynamic Discrete Choice Structural Models: A Survey”, Journal of Econometrics, 156: 38-67
6. Arcidiacono, P. and P. B. Ellickson (2011), "Practical Methods for Estimation of Dynamic Discrete Choice Models", Annual Review of Economics, 3, 363-394.
7. Arcidiacono, P. and R. A. Miller (2011), "Conditional Choice Probability Estimation of Dynamic Discrete Choice Models with Unobserved Heterogeneity", Econometrica, 79, 1823-1867.
8. Berndt, E. K., B. H. Hall, R. E. Hall, J. A. Hausman (1974). "Estimation and Inference in Nonlinear Structural Models", Annals of Economic and Social Measurement 3, 653-665.
9. Eckstein, Z. and K. Wolpin (1989), “The Specification and Estimation of Dynamic Stochastic Discrete Choice Models: A Survey”, Journal of Human Resources, 24: 562-598
10. Hong, H. and M. Shum (1998), "Structural Estimation of Auction Models", In: Patrone F., García-Jurado I., Tijs S. (eds) Game Practice: Contributions from Applied Game Theory. Theory and Decision Library (Series C: Game Theory, Mathematical Programming and Operations Research), vol 23. Springer, Boston, MA.
11. Hotz, V. J. and R. A. Miller (1993), "Conditional Choice Probabilities and the Estimation of Dynamic Structural Models", Review of Economic Studies, 60, 497-529.
12. Keane, M. P. and K. I. Wolpin (1997), "The Career Decisions of Young Men", Journal of Political Economy, 105, 473-522.
13. Lee, D. and K. I. Wolpin (2006), "Intersectoral Labor Mobility and the Growth of the Service Sector", Econometrica, 74, 1-46.
14. Llull, J. (2018), "Immigration, Wages, and Education: A Labor Market Equilibrium Structural Model", Review of Economic Studies, 85, 1852-1896.
15. Llull, J. (2018), "Selective Immigration Policies and the U.S. Labor Market”, mimeo, MOVE, Universitat Autònoma de Barcelona, and BSE.
16. Miller, R. A. (1997), “Estimating models of dynamic optimization with microeconomic data”, in M. Pesaran and P. Schmidt (eds.), Handbook of Applied Econometrics, Vol. 2, pp. 246-299
17. Todd, P. and K. Wolpin (2006), "Assessing the Impact of School Subsidy Program in Mexico: Using a Social Experiment to Validate a Dynamic Behavioral Model of Child Schooling and Fertility", American Economic Review, 96, 1384-1417.
18. Rust, J. (1987), "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher", Econometrica, 55, 999-1033.
19. Rust, J. (1994), “Structural Estimation of Markov Decision Processes”, in R. E. Engle and D. L. McFadden (eds.), Handbook of Econometrics, Vol. 4, Ch. 51.

About the Instructor

* Laptop required In order to participate in practical sessions, you must bring your own portable computer.

The use of survey data is becoming a common practice among economists and social scientists both at academic and professional level. The main characteristic of this data is that it contains qualitative information, making the use of the regression model not suitable when we deal with models where the dependent variable is either a choice (having or not a private health insurance) or a status (being or not unemployed), or where the dependent variable only takes non negative values and a significant percentage of the observations are zeroes (expenditure on some particular goods like tobacco).On the other hand, the use of information with a panel data structure is also becoming frequent because it allows to deal with some limitations of the estimation of models with cross-section data.

In this course we deal with estimation of these models, paying special attention to the interpretation of the estimates and the limitations of the different models in the literature.

The course is organized in four blocks: the first one (one and a half sessions) devoted to discrete choice models, including count data models; the second one (one and a half sessions) to limited-dependent variable models, including sample selection issues typical when dealing with cross-section data; the third one (one session) devoted to duration models (survival analysis) in which the dependent variable is the length of a spell in a particular status (e.g. unemployment) and we are interested in estimating how the duration in a particular status affects the probability of leaving that status; and the fourth block to an introduction to (static) panel data, paying attention to the econometric advantages of this type of information.

Practical sessions will be devoted to how to estimate these models using STATA and how to interpret the results. This will be done using real data for different fields: demand analysis, health economics, labour economics, among others. Empirical papers will also be described and discussed in the lectures.

Course Outline

1. Discrete Choice Models (I)

Binary choice models

• Linear Probability Model
• Utility Maximization Models: Probit and Logit
• Marginal Effects
• Selection Criteria
• Example: A model of housing tenure
• Example: Psychological pressure in competitive environments: penalty kicks

Multinomial Models

• Multinominal Logit
• Independence of Irrelevant Alternatives
• Marginal Effects
• Conditional Logit Model
• Nested Logit Model and Mixed Logit Model
• Example: A model of transport mode choice

Ordered Models

• Standard Ordered Model: Probit and Logit
• Marginal Effects
• Limitations of the Standard Ordered Model

Count Data Models

• Poisson Model
• Negative Binominal Models
• Zero Inflated Models
• Example: Number of inpatient stays in hospital

General References:

• Cameron and Trivedi (2005), Ch. 14, 15.1 to 15.9, 20
• Cameron and Trivedi (2010), Ch. 14, 15, 17
• Wooldridge (2010), Ch. 15.1 to 15.6, 15.9, 15.10, 19

Applications:

• Bourassa, S.C. (1995), “A Model of Housing Tenure Choice in Australia”, Journal of Urban Economics, 37, 161-175
• Apesteguía, J. and I. Palacios-Huerta (2010), “Psychological Pressure in Competitive Environment: Evidence from a Randomized Experiment”, American Economic Review, 100, 2548-2564
• Train, K. (1979), “A Comparison of the Predictive Ability of Mode Choice Models with Various levels of Complexity”, Transportation Research A, 13, 11-16
• Hayo, B. and W. Seifert (2003), “Subjective Economic Well-Being in Eastern Europe”, Journal of Economic Psychology, 24, 329-348
• Geil, P., A. Million, R. Rotten and K.F. Zimmermann (1997), “Economic Incentives and Hospitalization in Germany”, Journal of Applied Econometrics, 12, 295-311

2. Limited Dependent Variables Models (II)

Tobit Model

• Estimation
• Interpretation of the Coefficients
• Limitations
• Other Applications ot the Tobit Model
• Example: Gambling expenditure
• Example: Demand for Education

Sample Selection Model

• Estimation and Two-part models

Double-hurdle Models

• Estimation
• Particular Cases
• Example: A Willingness-To-Pay-model
• Example: Tobacco consumption

General References:

• Cameron and Trivedi (2005), Ch. 16.1 to 16.7
• Cameron and Trivedi (2010), Ch. 16.1 to 16.6
• Wooldridge (2010), Ch. 16.1 to 16.7, 18

Applications:

• Pérez, L. and B.R. Humphreys (2012), “The Income Elasticity of Lottery: New Evidence from Micro Data”, Public Finance Review, 39, 551-570
• Tansel, A. and F. Bircan (2006), “Demand for Education in Turkey: A Tobit Analysis of Private Tutoring Expenditures”, Economics of Education Review, 25, 303-313
• Castellanos,P., J. García and J.M. Sánchez (2011), “The Willingness to Pay to Keep a Football Club in a City: How Important Are the Methodological Issues”, Journal of Sports Economics, 12, 464-486
• Aristei, D. and L. Pieroni (2008), “A Double-Hurdle Approach to Modelling Tobacco Consumption in Italy”, Applied Economics, 40, 2463-2476
• Madden, D. (2008), “Sample selection versus two-part models revisited: The case of female smoking and drinking”, Journal of Health Economics, 27, 300-307

3. Duration Models

Basic Concepts

Continuous Time Models

• Exponential. Weibull, Log-logistic

Types of Data

• Longitudinal/retrospective
• Corss-sectional
• Estimation
• Heterogeneity

Examples

• Unemployment Duration
• Propensity to start smoking

General References:

• Cameron and Trivedi (2005), Ch. 17, 18.1 to 18.6
• Wooldridge (2010), Ch. 20

Applications

• Narendranathan, W., S. Nickell and J. Stern (1985), “Unemployment Benefits Revisited”, Economic Journal, 95, 307-329
• López, A. (2002), “How Important are Tobacco Prices in the Propensity to Start and Quit Smoking? An Analysis of Smoking Histories from the Spanish National Health Survey”, Health Economics, 11, 521-535

4. Panel Data (Static Models)

Panel Data versus Cross-Section

Linear Model: Basic Estimators

• OLS
• Between-group estimator
• Within-group estimator
• GLS (random effects model)
• Properties of the estimators

Correlation between Individual Effects and Regressors

• ​Hausman's Test
• IV Estimators when some Regressors are Time Invariant

Examples

• Earnings Equations
• Economic Model of Crime

General References:

• Cameron and Trivedi (2005), Ch. 21, 22
• Cameron and Trivedi (2010), Ch. 8, 9
• Wooldridge (2010), Ch. 10, 11
• Baltagi, B. (2013), Econometric Analysis of Panel Data, Wiley

Applications

• Cornwell, C. and Trumbull, W.N. (1994), “Estimating the economic model of crime with panel data”, Review of Economics and Statistics, 76, 360-366
• Hausman, J.A. and Taylor, W.E. (1981), “Panel data and unobservable individual effects”, Econometrica, 49, 1377-1398​

References

• Cameron, A.C. and Trivedi, P.K., Microeconometrics. Methods and Applications, Cambridge University Press, 2005
• Cameron, A.C. and Trivedi, P.K., Microeconometrics using STATA, STATA Press, 2010
• Wooldridge, J.M., Econometric Analysis of Cross-Section and Panel Data, MIT Press, 2010

About the Instructor

Jaume García-Villar is Professor of Economics at Universitat Pompeu Fabra and Affiliated Professor of the BSE. He received his PhD from the London School of Economics and Political Science (1985). He was President of the Spanish National Statistics Institute from 2008 until 2011.

Overview and Objectives

The main challenge for policy evaluation is to establish a causal link between interventions and outcomes. The objective of this course is to introduce the main econometric approaches used in the evaluation of public policies: randomized evaluations, natural experiments, the regression discontinuity design, selection on observables, difference-in-differences, and synthetic control methods. The course presents strengths and weaknesses of each approach in terms of internal and external validity. During the morning sessions, each approach will be presented and illustrated with specific examples in the areas of labor economics, health economics, and the economics of education. In the afternoon sessions, we will replicate the results of a prominent published study for each evaluation approach in Stata. Students are provided the corresponding data and code in advance so they can prepare.

Course Outline

1. Randomized Evaluations (Experiments)

• Duflo, E., R. Glennerster and M. Kremer, 2007, “Using Randomization in Development Economics Research: A Toolkit,” in Handbook of Development Economics, 4, Chapter 61, 3895-3962.
• Katz L. F., J. R. Kling, and J. B. Liebman, 2007, “Experimental Analysis of Neighborhood Effects,” Econometrica, 75 (1): 83-119.
• Krueger, A. B., 1999, “Experimental Estimates of Education Production Functions,” Quarterly Journal of Economics, 14 (2): 497-562.
• Miguel, E. and M. Kremer, 2004, “Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities,” Econometrica, 72 (1): 159-217.

2. Natural Experiments and the Problem of Weak Instruments

• Andrews I., Stock J. H. and Sun L., 2019, “Weak Instruments in IV Regression: Theory and Practice,” Annual Review of Economics, 11: 727-753.
• Angrist J. D. and W. N. Evans, 1998, “Children and Their Parents’ Labor Supply: Evidence from Exogenous Variation in Family size,” American Economic Review, 88: 450-477.
• Chernozhukov. V. and C. Hansen, 2008, “The Reduced Form: A Simple Approach to Inference with Weak Instruments,” Economics Letters, 100: 68-71.
• Lee, D. S., M. J. Moreira, J. McCrary, and J. Porter, 2020, “Valid t-ratio Inference for IV”, Papers 2010.05058, arXiv.org.
• Montiel Olea J and C. Pflueger, 2013, “A Robust Test for Weak Instruments”, Journal of Business and Economic Statistics, 31: 358-369.
• Stock, J. H. and M. Yogo, 2005, “Testing for Weak Instruments in Linear IV Regression,” in D. W. K. Andrews (ed.), Identification and Inference for Econometric Models, Chapter 5, New York, Cambridge University Press, 109-120.

3. Regression Discontinuity Designs

• Angrist, J. D. and V. Lavy, 1999, “Using Maimonides’ Rule to Estimate the Effect of Class Size on Scholastic Achievement,” Quarterly Journal of Economics, 114(2): 533-775.
• Calonico, S., M. D. Cattaneo, M. H. Farrell, and R. Titiunik, 2019, “Regression Discontinuity Designs Using Covariates”, Review of Economics and Statistics, 101 (3): 442-451.
• Fredriksson, P., Öckert B. and H. Oosterbeek, 2013, “Long-Term Effects of Class Size,” Quarterly Journal of Economics, 128 (1): 249-285.
• Lee, D. S., and T. Lemieux, 2010, “Regression Discontinuity Designs in Economics,” Journal of Economic Literature, 48 (2): 281-355.
• Ludwig, J. and D. L. Miller, 2007, “Does Head Start Improve Children’s Life Chances? Evidence from a Regression Discontinuity Design,” Quarterly Journal of Economics, 122 (1): 159-208.

4. Selection on Observables (Linear Regression, Adjusting for Unobservables, Exact Matching, Propensity Score Matching)

• Angrist, J.D. and J.-S. Pischke, 2009, Mostly Harmless Econometrics, An Empiricist´s Companion, Princeton University Press.
• Becker, S. and A. Ichino, 2002, “Estimation of Average Treatment Effects Based on Propensity Scores,” The Stata Journal, 2(4): 358-377.
• Dehejia, R. and S. Wahba, 1999, “Causal Effects in Non-experimental Studies Re-evaluating the Evaluation of Training Programs,” Journal of the American Statistical Association, 94 (448): 1053-1062.
• Imbens, G. W., 2015, “Matching Methods in Practice: Three Examples,” Journal of Human Resources, 50 (2): 373-419.
• Oster E., 2019, “Unobservable Selection and Coefficient Stability: Theory and Evidence,” Journal of Business and Economics Statistics, 37 (3): 187-204.

5. Difference-in-Differences, Event Studies, Synthetic Control Methods

• Abadie, A., 2021, “Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects,” Journal of Economic Literature, 59 (2): 391-425.
• Abadie, A., A. Diamond and J. Hainmueller, 2010, “Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program,” Journal of the American Statistical Association, 105: 493-505.
• Borusyak, K., X. Jaravel and J. Spiess, 2021, “Revisiting Event Study Designs: Robust and Efficient Estimation,” Working Paper.
• De Chaisemartin, C. and X. D'Haultfœuille, 2020, “Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects.” American Economic Review, 110 (9): 2964-2996.
• Duflo E., 2001, “Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence from an Unusual Policy Experiment,” American Economic Review, 91(4): 795-913.
• Goodman-Bacon, A., 2018, “Difference-in-Differences with Variation in Treatment Timing,” Journal of Econometrics, 225 (2): 254-277.
• Jacobson, L. S., R. J. LaLonde and D. G. Sullivan, 1993, “Earnings Losses of Displaced Workers,” American Economic Review, 83(4): 685-709.
• Sun, L. and S. Abraham, 2021, “Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects”, Journal of Econometrics, 225 (2): 175-199.

References

• Abadie, A. and M. D. Cattaneo, 2018, “Econometric Methods for Program Evaluation,” Annual Review of Economics, 10: 465-503.
• Imbens, G. W. and J. M. Wooldridge, 2009, “Recent Developments in the Econometrics of Program Evaluation,” Journal of Economic Literature, 47 (1): 5-86.

About the Instructor

Albrecht Glitz is Associate Professor at Universitat Pompeu Fabra, Affiliated Professor of the Barcelona School of Economics, and Researcher at IPEG. He received his PhD from University College London in 2008. His research interests include labour economics, the economics of migration, and microeconometrics. His work has been published in the American Economic Review, the Review of Economic Studies, and the Journal of Labor Economics, among others.

* Laptop required In order to participate in practical sessions, you must bring your own portable computer.

This course considers methodological and substantive issues concerning the analysis of panel data. It starts by reviewing basic panel data models emphasizing the benefits and limitations of panel data over time series or cross-section data. Basic estimation and testing methods for random and fixed effects models are reviewed and illustrated using empirical applications using Stata and EViews. Next, problems of endogeneity in panel models are studied and panel instrumental variable estimation methods as well as Hausman type tests are reviewed and applied using an empirical application. The course also covers an introduction to dynamic panel data models. Finally, for large macro panels with a large number of countries observed over a long time series, we study issues of non-stationarity.

Course Outline

1. Basic Review of Panel Data Methods: Estimation and Test of Hypotheses
2. Simultaneous Equations and Endogeneity in Panel Data Models
3. Dynamic Panel Data: introduction
4. Nonstationary Panels

Basic Reference

Econometric Analysis of Panel Data, 6th edition (included in the course fees)
ISBN: 978-3-030-53952-8
Badi H. Baltagi
© 2021

Optional Text for Practical Sessions

A Companion to Econometric Analysis of Panel Data
Badi Baltagi
ISBN: 978-0-470-74403-1
© 2009

About the Instructor

Badi Baltagi is Distinguished Professor of Economics at Syracuse University and Part-time Chair in Economics at the University of Leicester. He holds a PhD in Economics from the University of Pennsylvania. He is Senior Research Associate at the Center for Policy Research at Syracuse University and is a Research Fellow at several other institutions. He is editor of Economics Letters, and on the Editorial Board of Econometric Reviews. He is a fellow of the Journal of Econometrics as well as Econometric Reviews and a recipient of the Multa and Plura Scripsit Awards from Econometric Theory. Also fellow of the Advances in Econometrics and recipient of the Distinguished Authors Award from the Journal of Applied Econometrics.