Research in Applied Econometrics Chapter 0. Introduction

(1)

Research in Applied Econometrics Chapter 0. Introduction

Pr. Philippe Polomé, Université Lumière Lyon 2

M1 APE Analyse des Politiques Économiques M1 RISE Gouvernance des Risques Environnementaux

2018– 2019

(2)

Plan

I

Motivation

I

Organisation

(3)

Course Objectives & Motivations

I

Class in Econometrics

I In a unit of English language

I

Goal: Expose students to applied econometrics in English

I Applied examples with environmental economics data

I Students should improve both their applied econometrics skills and their English level

I Attendance and interactions in class

I

Focus on applied techniques: Introduction to R

I More on that later

I

Context : ex ante valuation of public (environmental) policies

I Contingent valuation / stated preferences

I In econometrics details I With R commands I With data & examples

(4)

The relevance of valuation studies

I

Cost-benefit analysis

I Newly in France: public project with a “déclaration d’utilité publique” have to justify that Benefit > Cost

I For market and nonmarket goods & services

I Including e.g. value of human life, ecosystem services, patrimonial

& heritage values I In principle

I How do we compute that ?

I That includes environmental “services”, e.g. ecosystem functions I But also all kinds of benefits & costs, e.g. a prison removes

criminal from society and helps their rehabilitation I “valeurs tutélaires” (guidelines) & consensual discount rate I

Damage assessment for non-market goods

I France introduced a few years ago the principles of environmental damage and compensation in kind

I well-embodied in US legislation I not so much in EU legislation

I

Greening the National Accounts

(5)

Course Plan

1.

Introduction to R

2.

Nonmarket valuation basic theory

I French tend to say “évaluation”

I English stresses the idea of valuing I “assigning a value”

3.

Contingent valuation

I Best-known technique 4.

(Choice experiment)

I Harder econometrics

(6)

Course Organization

I

6 lectures of 3.5 hours each

I Every week

I “Dispense d’assiduité” not possible for language courses I Bring your laptop as much as possible

I

Do not forget it is a language course

I Please interrupt me when you don’t understand

(7)

Evaluation: “Contrôle continu” in class for 100%

I

About 20’ at some point of each lecture

I Beginning, end or middle

I On what we have seen during that lecture&the previous one (not several)

I

If you miss one, you get zero at that one

I The 1st one is just practice

I

No final exam in “first session” in Decembre

I “Rattrapage” in June

I

It is

super important

that you read / study the class notes before coming to class

I That is why we do CC

I

I will try to correct the tests as much as possible

(8)

References

I

Aizaki et.al. Stated Preference Methods Using R. Chapman and Hall/CRC, 20140815. VitalBook file.

I Use DCchoice-package {DCchoice} in R I Base documentation in R

I

Kleiber & Zeilis, Applied Econometrics with R, Springer, 2008

I

Wooldridge, J. Introductory Econometrics : A Modern

Approach, Michigan State University, 2012

I Click this link

I BU Chevreul[330.015.2 WOO] (1)

I Not [330.015.2 WOO] (2) Econometric analysis of cross section and panel data

(9)

Install R

I

Come to class w/ a laptop

I R & R-studio installed & up-to-date I

R @ www.r-project.org/

I R-Studio https://www.rstudio.com/

I IDE (integrated development environment) I Not a Graphical User Interface, but more useful I Packages “add functionalities”

I Most often from within R-studio I Start R-Studio

I R-Studio calls R

(10)

Presenting R-studio: 4 windows

(11)

R-Studio Upper Left Window: editor

I

Invoked with any of 2 leftmost buttons of the toolbar (New or Load)

I Color-coded, with online help & command recognition I

Programming is written in the editor

I Programming = sequence of commands in a text file “script”

I with an .R extension

I This file is saved for further use, between “sessions”

I Commandsare passed by e.g.plot(x)

I The editor recognizes command and colors them inblue I Commands are executed in the editor byCMD←-row by row I Command results may be stored inobjectswith <-

I y_lm <- lm(y~x1+x2)

I Several command files may be simultaneously open I tabs

(12)

R-studio Windows

I

Lower Left :

console

I Print out command results from editor

I Usual way to write code : write one or a few lines, test it I Write commands for immediate execution (with←-)

I Does not stay in memory

I

Upper Right

I Environment: List in memory I Can be data or results or functions I Within a project (later) or not I Command history

I Can be reused

(13)

R-studio Lower Right Window : 5 tabs

I

Files within the project

I

Visualisations of Plots

I

Packages that are present

I Loaded if checked square I Install button

I Click it (you must be connected) I Type swirl & follow instructions

I

Help

I

Viewer

I to view local web content (if you edit webpages) I

These 5 tabs have in common the Search window

(14)

First commands: Project

I

A project is a file that refers to a collection of files

I R command files .R, data files, results

I

There’s an icon in the upper-right corner of R-Studio

I Click it & create a project “RAE”

I Where you create it, that is your work directory

I Do not use the desktop, the root, or any hard-to-find location I Download the RAE2017.R on my courses’ site

I Into the same directory as your project I Open it from R-studio Editor : Icon upper left

I

R-Studio recalls the projects

I You can go from one to another

I All the files written on disk remain available

(15)

First commands

I

Some manipulation in Console

I writeSys.setenv(LANG = "fr")

I Sets R Console in French, only for “core”, not for most packages I R-Studio is only in English

I writeinstall.views("Econometrics")

I For about all the packages we will ever need I This is long : don’t do that in class ! I In the futureupdate.views("Econometrics")

I

Editor

I Write here things that you intend to reuse I AvoidFrench symbols é, è, ê, ë, à, ù, ç, ...

I Avoidsymbols like #, $, &, -... if you are unsure of their use I Try to stickto unaccented latin characters (i.e. US alphabet) I CAPITALISATION is important

I

Starting a row w/ # indicates to R that it is a commentary

I Green-colored, will not be executed

(16)

SWIRL: set of basic training modules

I

Install swirl as any package from R-studio (should be installed by now)

I Then type

I install_course("R Programming") I install_course("Regression_Models")

I Other courses https://github.com/swirldev/swirl_courses I About SWIRL: http://swirlstats.com/students.html

I Slides https://github.com/DataScienceSpecialization/courses I

Self-training : Type

swirl( )

in concole

I do course 1: R programming, Lessons 1-9 + 14 I By yourself, from home, when you have time I We will (re)do Lesson 1 in class

(17)

Some ressources about R on the web

I

Use Google !

I Ask question based on English keywords I e.g. “R read Stata data”

I

From R home page www.r-project.org

I Getting help, Manuals, FAQS...

I

A few interesting links

I Quick-R www.statmethods.net/index.html I http://stats.idre.ucla.edu/r/

I http://varianceexplained.org/RData/

I www.r-bloggers.com I R for economists

I www.mayin.org/ajayshah/KB/R/R_for_economists.html

I

En français: forget about French for R

(18)

To sum up

I

For the 1st course you have to have

I installed R & R-Studio on your machines I From R-Studio

I install.views("Econometrics") I install swirl

I In swirl :

I install the 2 modules (programming & regressions) I do course 1: R programming, Lessons 1-9 + 14

I

Install packages : DCchoice, Ecdat, stats

I

Created your project & opened RAE2017.R

I

Classes are mandatory

I There is CC in each one, no final exam