Topics in Applied Econometrics : Panel Data
M2 Equade & M2 GAEXA
Pr. Philippe Polomé, Université Lumière Lyon 2
2015 – 2016
Chapter 0. Introduction
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Presentation
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Organization
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Motivation
Myself
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Professor Université Lumière Lyon 2
I Labo GATE-LSE UMR 5824 CNRS - UL2 - UJM
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M2 RISE “Risque et Environnement” risk.ish-lyon.cnrs.fr
My Research
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Environmental Economics / Ecological Governance
I Prosocial Behaviors
I Nonmarket Valuation
I Environmental risk
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Micro Analysis of Farms Environmental Decisions
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Applied Econometrics
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Research Internship Analyse économétrique des effets des actions publiques sur les dommages
I 5 mois financés LabEx IMU
I Disc Princ : Écon - Disc Sec. : Histoire
I Collecte ou extraction de données chiffrées
I pour constituer des séries temporelles de dommages (écono., physiques...)
I et de régresseurs: débit des rivières, accidents... actions publiques, infrastructures...
I effets des actions publiques sur les dommages, corrélation des dommages entre eux (cascades NaTech)
You
Just a discussion
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What do you think you will find in this course
I What do you want to do with it ?
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What econometrics problems are you most interested in ?
I Which one do you expect to deal with in your professional life ?
Course Organization
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2 M2 : EQUADE & GAEXA : same content & evaluation
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Course available via www.gate.cnrs.fr/perso/polome
I Not quite up to date
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4 classes on theory
I Tuesdays 29/09, Mondays 05/10, 12/10, 19/10 @ D117
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2 classes for papers presentation
I MaybeMondays 09/11, 16/11 @ D117
I Groups of 3 students prepare and present areportusing panel data techniques in English
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Written exam early december TBA
Report Format
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Click on GUIDELINES
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One report / group, in English & no longer than 10 pages of text
I + front matter, bibliography and annexes
I the shorter the better
I Report format will be evaluated
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Only pdf format sent by mail to remise@compilatio.net
I Mail subject : cfl15
I Plagiarism detection tool
I Until 04/11: reports sent in after that are penalized
I Send your report as many times as you want
I I look only the last one
I Report nameYour3Names.pdf
Report Content
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The research must be econometrically sound
I Present your data properly
I Present a meaningful regression
I some theory to explain why you suspect a specific relation
I Why is your estimator better than others ?
I Based on theory : Why is it consistent/efficient ? I
Can be original work
I The source of the data MUST be identified
I May be on the same topic as your research paper
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Can be based on a paper
I Properly cite & send a pdf of the paper with your report
I Replicate at least some of the results
I Do not reproduce what the authors have done, something simpler is enough
Data sources
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Find your own data
I DATA: links to data sources
I Econometrics softwares Gretl and R (below) include many panel data sets
I Google for reading & converting data files
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Groups should coordinate to present different data sets
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Artificial data sets (Monte-Carlo experiments) are not accepted
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Journals with downloadable data @ DATA
I Consult your library for availability of the journal and the specific paper
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Data must be handed in with the report
I In a separate file in the format of the software you used
Forest Cover : I am interested that you examine
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World bank data @
I data.worldbank.org/indicator/AG.LND.FRST.K2?page=4
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In search for
Environmental Kuznetz CurvesEKC
I For the world or regions or countries
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Larger levels of per capita income associated with gradually lower levels of pollutants
yt = 0+ 1GDPht+ 2GDPh2t + xt+✏t
Presentations
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30 minutes + 15 minutes discussion per group
I 10 groups of 3 students
I You may mix GAEXA & EQUADE
I I will not look into groups composition: organize yourselves I
Presentation by one, two or all the students in the group
I in English
Evaluation
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10 points for the exam
I Will include questions about the presentations
I Especially those points not well explained
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10 points for the report
2 Data documentation & presentation, incl graphics 2 Economic model construction & documentation
3 Econometrics quality : correct models (2), advanced stufffor +1 2 Presentation & dicussion of econometrics results, incl graphics (no paste) 1 Proper identification of issues after analysis (no issue =0)
1 Professional report format Separately : Data & Code (- 2 each)
Software Packages
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Stata in class
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Gretl http://gretl.sourceforge.net/
I Similar to Stata but simpler
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R http://www.r-project.org/
I Self-teaching page http://www.ats.ucla.edu/stat/R/
I Harder to learn, but more tools than Stata
I Rstudio makes it simpler
I Panel : plm package
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R & Gretl
I open-source, free, multi-platform, multi-lingual
I include data sets that can be used for your research
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Complete code must be handed in in an annex of the report
Course Content & Motivation
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Econometrics course in English on Panel Data regressions
I Cameron, A.C. & P.K. Trivedi, Microeconometrics, Cambridge, 2005, 2006
I Wooldridge, J. Econometric Analysis of Cross Section and Panel Data, MIT Press, 2001
Panel data Informal
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Panel data = repeated observations on the same cross section
I With possibleattrition
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Individuals or firms in microeconomics applications, observed for several time periods
I Other terms : longitudinal data and repeated measures
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This course : data from a short panel :
I Large cross section of individuals observed for a few time periods
I Time series issues assumed addressed properly
I Rather than a long panel such as a small cross section of countries observed for many time periods
First advantage of panel data : Precision
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More observations because of pooling several time periods of data for each individual
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For valid statistical inference
I Control for correlation of errors over time for a given individual
I The usual formula for OLS standard errors in a pooled OLS regression
I typically overstates the precision gains =)
I underestimated standard errors
I inflated t-statistics
Second advantage of panel data : Unobserved Heterogeneity
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Consistent estimation under unobserved individual heterogeneity correlated with regressors
I unobserved individual-specific effectsin a Panel Data setting
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With cross section, unobserved heterogeneity leads to omitted variables bias
I Might be corrected by instrumental variables methods
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Data from a short panel, with as few as 2 periods,
I alternative to IV
I unobserved individual-specific effects must be additive and time-invariant
Example
Single period
Example
Several periods : panel data may lead to reversed conclusions
Third advantage of panel data: Dynamics
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Learning more about the dynamics of individual behavior
I For ex. a cross section may yield a poverty rate of 20%
I need panel data to determine whether thesame20% are in poverty each year
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Panel data may determine whether high serial (=across time) correlation of individual earnings is due to
I anindividual specific tendency to have low earnings
I unobserved (time-invariant) heterogeneity
I aconsequenceof havingpastlow earnings
I “true” serial correlation
Chapter Contents
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Ch 1 : linear regression
I Key results for linearpanel data regressions
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Ch 2 : extensions for Endogenous regressors
I Dynamicpanels which allow for Markovian (“previous period”) dependence structure of current variables
I Analysis in Generalized Method of MomentsGMMframework
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Ch 3 : extensions for binary panel data models
I Ch 1 & 2 do not extend tononlinearpanel models
I Fewer results forlimited dependent variablepanel models
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Persistent themes
I Fixed effectsandrandom effectsmodels
I importance of panel-robustinference