Panel Data Econometrics
School of Economics and Management
University of Geneva (2010, 2018)
Notes: these teaching resources correspond to the course "Advanced Econometrics II", that I taught in 2018 in the master program of the School of Economics and Management of the University of Geneva. This lecture is exclusively devoted to panel data econometrics.
For more resources (in French) about panel data econometrics, see also the webpage devoted to the course "Économétrie des Donnés de Panel" (University Paris Dauphine).
Panel data econometrics
Geneve_Chapitre0.pdfGeneral introduction on panel data econometrics
Baseline definitions (micro and macro panels, balanced panel, etc.)
Advantages of Panel Data Sets and Panel Data Models
Issues Involved in using Panel Data
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Chapter 1: Linear panel data models
Section 1: Specification tests and analysis of covariance
Section 2: Linear unobserved effects panel data models
Section 3: Fixed effects estimation methods
Section 4: Random effects estimation methods
Section 5: Specification tests: random or fixed effects?
The Mundlak's specification and Hausman's test
Section 6: Heterogeneous panel data models
Random Coefficient Models
Mean group estimation
Grouped Patterns of Heterogeneity (Bonhomme and Manresa, 2015)
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Geneve_Chapitre1.pdfGeneve_Chapitre2.pdfChapter 2: Dynamic panel data models
Section 1: The dynamic panel bias
Section 2: The Instrumental Variable (IV) approach
Anderson and Hsiao (1982) IV approach
Section 3: The Generalized Method of Moment (GMM) approach
GMM estimators for dynamic panel data models: Arellano and Bond (1991), Arellano and Bover (1995), Ahn and Schmidt (1995)
System GMM (Blundell and Bond, 2000)
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Chapter 3: Panel Threshold Regression Models
Section 1: Introduction
Section 2: The Panel Threshold Regression (PTR) Model
Section 3: The Panel Smooth Transition Regression (PSTR) Model
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Geneve_Chapitre3.pdfAdditional resources
Matlab code for the Panel Threshold Regression (PTR) model. Example: Candelon B., Colletaz G., and Hurlin C. (2013), Network Effects and Infrastructure Productivity in Developing Countries, Oxford Bulletin of Economics and Statistics, 75(6), 887-913.
Matlab code for the Panel Smooth Transition Regression (PSTR) model. Example: Hurlin C., Rabaud I., and Fouquau J. (2008), The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach, Economic Modelling, 25, 284-299.
Panel unit root tests
Here are some links to download Matlab code for first and second generation panel root tests.
Levin A., Lin C., and Chu C. (2002), Unit Root Tests in Panel Data: Asymptotic and Finite-Sample Properties, Journal of Econometrics, 108, 1-24. Matlab code.
Maddala G. and Wu S. (1999), A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test, Oxford Bulletin of Economics and Statistics, 61, 631-652. Matlab code.
Choi I. (2001), Unit Root Tests for Panel Data, Journal of International Money and Finance, 20, 249-272. Code Matlab.
Im K., Pesaran H., et Shin Y. (2003), Testing for Unit Roots in Heterogeneous Panels, Journal of Econometrics, 115, 53-74. Matlab code.
Moon H. and Perron B. (2004), Testing for a Unit Root in Panels with Dynamic Factors, Journal of Econometrics, 122, 81-126. Matlab code.
Hurlin, C (2010), What would Nelson and Plosser find had they used Panel Unit Root Tests?, Applied Economics, 42(12), 1515 - 1531. Matlab code.
Panel Granger Non-Causality test
Dumitrescu E. and Hurlin C. (2012), Testing for Granger Non-causality in Heterogeneous Panels, Economic Modelling, 29, 1450-1460.
Matlab code for the Dumitrescu and Hurlin (2012) panel Granger non-causality test.
The Dumitrescu and Hurlin (2012) panel Granger non-causality test is also available under Eviews, R (pgrangertest), and Stata software.