Topics in Applied Econometrics : Panel Data

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Topics in Applied Econometrics : Panel Data

M2 Equade & M2 GAEXA

Pr. Philippe Polomé, Université Lumière Lyon 2

2015 – 2016

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Chapter 0. Introduction

I

Presentation

I

Organization

I

Motivation

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Myself

I

Professor Université Lumière Lyon 2

I Labo GATE-LSE UMR 5824 CNRS - UL2 - UJM

I

M2 RISE “Risque et Environnement” risk.ish-lyon.cnrs.fr

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My Research

I

Environmental Economics / Ecological Governance

I Prosocial Behaviors

I Nonmarket Valuation

I Environmental risk

I

Micro Analysis of Farms Environmental Decisions

I

Applied Econometrics

I

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)

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You

Just a discussion

I

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

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2 M2 : EQUADE & GAEXA : same content & evaluation

I

Course available via www.gate.cnrs.fr/perso/polome

I Not quite up to date

I

4 classes on theory

I Tuesdays 29/09, Mondays 05/10, 12/10, 19/10 @ D117

I

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

I

Written exam early december TBA

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Report Format

I

Click on GUIDELINES

I

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

I

Only pdf format sent by mail to [email protected]

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

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Report Content

I

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

I

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

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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

I

Artificial data sets (Monte-Carlo experiments) are not accepted

I

Journals with downloadable data @ DATA

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

I

Data must be handed in with the report

I In a separate file in the format of the software you used

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Forest Cover : I am interested that you examine

I

World bank data @

I data.worldbank.org/indicator/AG.LND.FRST.K2?page=4

I

In search for

Environmental Kuznetz Curves

EKC

I For the world or regions or countries

I

Larger levels of per capita income associated with gradually lower levels of pollutants

yt = ₀+ ₁GDPht+ ₂GDPh²_t + xt+✏t

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Presentations

I

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

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Evaluation

I

10 points for the exam

I Will include questions about the presentations

I Especially those points not well explained

I

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)

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Software Packages

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Stata in class

I

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 Stata

I Rstudio makes it simpler

I Panel : plm package

I

R & Gretl

I open-source, free, multi-platform, multi-lingual

I 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

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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

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Panel data Informal

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Panel data = repeated observations on the same cross section

I With possibleattrition

I

Individuals or firms in microeconomics applications, observed for 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 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

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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

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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

I

With cross section, unobserved heterogeneity leads to omitted variables bias

I Might be corrected by instrumental variables methods

I

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

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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

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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

I

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

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Chapter Contents

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Ch 1 : linear regression

I Key results for linearpanel data regressions

I

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

I

Ch 3 : extensions for binary panel data models

I Ch 1 & 2 do not extend tononlinearpanel models

I Fewer results forlimited dependent variablepanel models

I

Persistent themes

I Fixed eﬀectsandrandom eﬀectsmodels

I importance of panel-robustinference