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Topics in Applied Econometrics : Panel DataM2 Equade & M2 GAEXA

Pr. Philippe Polomé, Université Lumière Lyon 2

2015 – 2016

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Chapter 0. Introduction

I PresentationI OrganizationI Motivation

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MyselfI Professor Université Lumière Lyon 2

I Labo GATE-LSE UMR 5824 CNRS - UL2 - UJMI M2 RISE “Risque et Environnement” risk.ish-lyon.cnrs.fr

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My ResearchI Environmental Economics / Ecological Governance

I Prosocial BehaviorsI Nonmarket ValuationI Environmental risk

I Micro Analysis of Farms Environmental DecisionsI Applied EconometricsI Research Internship Analyse économétrique des effets des

actions publiques sur les dommagesI 5 mois financés LabEx IMUI Disc Princ : Écon - Disc Sec. : HistoireI Collecte ou extraction de données chiffrées

Ipour constituer des séries temporelles de dommages (écono.,

physiques...)

Iet de régresseurs: débit des rivières, accidents... actions

publiques, infrastructures...

Ieffets des actions publiques sur les dommages, corrélation des

dommages entre eux (cascades NaTech)

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You

Just a discussionI What do you think you will find in this course

I What do you want to do with it ?

I What econometrics problems are you most interested in ?I Which one do you expect to deal with in your professional life ?

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Course Organization

I 2 M2 : EQUADE & GAEXA : same content & evaluationI Course available via www.gate.cnrs.fr/perso/polome

I Not quite up to date

I 4 classes on theoryI Tuesdays 29/09, Mondays 05/10, 12/10, 19/10 @ D117

I 2 classes for papers presentationI

Maybe Mondays 09/11, 16/11 @ D117I Groups of 3 students prepare and present a report using panel

data techniques in English

I Written exam early december TBA

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Report FormatI Click on GUIDELINESI One report / group, in English & no longer than 10 pages of

textI + front matter, bibliography and annexes

Ithe shorter the better

I Report format will be evaluated

I Only pdf format sent by mail to remise@compilatio.netI Mail subject : cfl15I Plagiarism detection toolI

Until 04/11 : reports sent in after that are penalizedI

Send your report as many times as you want

II look only the last one

I Report name Your3Names.pdf

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Report ContentI The research must be econometrically sound

I Present your data properlyI Present a meaningful regression

Isome 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 workI The source of the data MUST be identifiedI May be on the same topic as your research paper

I Can be based on a paperI Properly cite & send a pdf of the paper with your reportI Replicate at least some of the results

IDo not reproduce what the authors have done, something

simpler is enough

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Data sources

I Find your own dataI DATA: links to data sourcesI Econometrics softwares Gretl and R (below) include many

panel data setsI Google for reading & converting data files

I Groups should coordinate to present different data setsI Artificial data sets (Monte-Carlo experiments) are not acceptedI Journals with downloadable data @ DATA

I Consult your library for availability of the journal and thespecific paper

I Data must be handed in with the reportI In a separate file in the format of the software you used

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Forest Cover : I am interested that you examineI World bank data @

I data.worldbank.org/indicator/AG.LND.FRST.K2?page=4I In search for Environmental Kuznetz Curves EKC

I For the world or regions or countriesI Larger levels of per capita income associated with gradually

lower levels of pollutants

yt = �0 + �1GDPht + �2GDPh2t + �xt + ✏t

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Presentations

I 30 minutes + 15 minutes discussion per groupI 10 groups of 3 students

IYou may mix GAEXA & EQUADE

II will not look into groups composition: organize yourselves

I Presentation by one, two or all the students in the groupI in English

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Evaluation

I 10 points for the examI Will include questions about the presentationsI Especially those points not well explained

I 10 points for the report

2 Data documentation & presentation, incl graphics2 Economic model construction & documentation3 Econometrics quality : correct models (2), advanced stuff for +12 Presentation & dicussion of econometrics results, incl graphics (no paste)1 Proper identification of issues after analysis (no issue =0)1 Professional report formatSeparately : Data & Code (- 2 each)

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Software Packages

I Stata in classI Gretl http://gretl.sourceforge.net/

I Similar to Stata but simpler

I 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 StataI Rstudio makes it simplerI Panel : plm package

I R & GretlI open-source, free, multi-platform, multi-lingualI include data sets that can be used for your research

I Complete code must be handed in in an annex of the report

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Course Content & Motivation

I Econometrics course in English on Panel Data regressionsI

Cameron, A.C. & P.K. Trivedi, Microeconometrics,

Cambridge, 2005, 2006

I Wooldridge, J. Econometric Analysis of Cross Section andPanel Data, MIT Press, 2001

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Panel data Informal

I Panel data = repeated observations on the same cross sectionI With possible attrition

I Individuals or firms in microeconomics applications, observedfor several time periods

I Other terms : longitudinal data and repeated measures

I This course : data from a short panel :I Large cross section of individuals observed for a few time

periodsI

Time series issues assumed addressed properly

I Rather than a long panel such as a small cross section ofcountries observed for many time periods

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First advantage of panel data : Precision

I More observations because of pooling several time periods ofdata for each individual

I For valid statistical inferenceI Control for correlation of errors over time for a given individualI The usual formula for OLS standard errors in a pooled OLS

regressionI

typically overstates the precision gains =)I

underestimated standard errors

Iinflated t-statistics

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Second advantage of panel data : Unobserved Heterogeneity

I Consistent estimation under unobserved individualheterogeneity correlated with regressors

Iunobserved individual-specific effects in a Panel Datasetting

I With cross section, unobserved heterogeneity leads to omittedvariables bias

I Might be corrected by instrumental variables methods

I Data from a short panel, with as few as 2 periods,I alternative to IVI unobserved individual-specific effects must be additive and

time-invariant

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Example

Single period

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Example

Several periods : panel data may lead to reversed conclusions

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Third advantage of panel data: Dynamics

I Learning more about the dynamics of individual behaviorI For ex. a cross section may yield a poverty rate of 20%I need panel data to determine whether the same 20% are in

poverty each year

I Panel data may determine whether high serial (=across time)correlation of individual earnings is due to

I an individual specific tendency to have low earningsI

unobserved (time-invariant) heterogeneity

I a consequence of having past low earningsI

“true” serial correlation

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Chapter Contents

I Ch 1 : linear regressionI Key results for linear panel data regressions

I Ch 2 : extensions for Endogenous regressorsI

Dynamic panels which allow for Markovian (“previous period”)dependence structure of current variables

I Analysis in Generalized Method of Moments GMM framework

I Ch 3 : extensions for binary panel data modelsI Ch 1 & 2 do not extend to nonlinear panel modelsI Fewer results for limited dependent variable panel models

I Persistent themesI

Fixed effects and random effects modelsI importance of panel-robust inference