Data analysis using Stata , AMSE Master (M1), Spring semester

Transcription

1 Data analysis using Stata , AMSE Master (M1), Spring semester Notes Marc Sangnier

2 Data analysis using Stata Virtually infinite number of tasks for data analysis. Almost infinite number of commands in Stata (including user-written ones). Objectives of this course 1. Grasping Stata main features and learning how to use it. 2. Learning how to conduct empirical analysis (organization, etc.).

3 Outline of the course Introducing Stata. Using Stata! Tasks oriented sessions (mini-projects and exercises); 2 or 3-student Stata project.

4 Grading Exercices and mini-projects (30%). To be delivered by the end of each session. Stata project (70%) Groups of 2 or 3 (randomly constructed). Session time to work on the project and instructor s help. To be delivered 1 week after the last session. Detailed instructions will be provided in due time.

5 Introduction Overview of Stata. Stata s main commands (and principles). Organizing empirical work.

6 Overview of Stata Strengths of Stata: Very good balance between user-friendlyness and programing flexibility Many pre-programmed features Infinitely extensible Large users community Cross-platform compatibility Few cross-version compatibility issues Let us explore Stata.

7 Stata resources 1. Many online tutorials (including some in French): ta/home.aspx o-antoine/fr/documents/stata.pdf

8 Stata resources 2. Stata official documentation 3. Stata community Statalist: just google «Stata ANYTHING»!

9 Stata s main commands Stata default file format:.dta use use "D:\...\Cours\ AMU Data Analysis using Stata\Files\Lecture 1\census2.dta" use insheet for delimited format data infile, infix for undelimited data

10 Stata s main commands browse or bro or br help describe Variables types list generate and replace summarize tabulate

11 Stata s main commands rename drop keep Lists: wildcard * From var1 to var5 : var1-var5 (order matters) egen

12 Stata s main commands Label values and variables label var text label define labelname rules label values var labelname recode as a command or as a function encode egen functions collapse

13 Stata s main commands Loops forvalues i = foreach x in foreach var of varlist

14 Organizing empirical work Rules and good practices to ensure easy replicability. When going back to your work; When adapting the analysis to other data; When checking someone elses s work.

15 Directories structure ".../My_project" Report (or Paper) Empirics (or Data Analysis) Data Source Temp(orary) Final Programs (or Do-files) Out(put)

16 Do-files and relative paths Programs (or Do-files) Numbered programs (order matters) Including a master file (e.g. 0_master.do) Change directory: cd Relative paths: Down " / " ; Up ".. " The master file allows to run the complete analysis and describe other programs.

17 The "source" folder Original files exactly as obtained, received or downloaded (change of name is allowed). Subfolders allowed if necessary Each file must me accompanied by a name_of_the_file_readme.txt (or name_of_the_file_notes.txt) that specifies all information about the origin of the file (provider, date, etc.). The "source" folder is a read-only directory!

18 Datasets manipulation save (, replace), outsheet clear merge append reshape Temp(orary), Source and Final folders! Basic data checks: duplicates, distinct

19 Do-files Typical "0_master.do" clear [set more off] cd ".../My_project" do "Programs/1_data_preparation.do" * Program that prepares data. do "Programs/2_data_analysis.do" * Program that analyzes data.

20 Do-files Typical "#_anything.do" clear * Data preparation ********************* * Step1 ******** command * comment of the command

21 Problem 1 Problem 2

22 Time series functionalities tsset timevar tsset panelvar timevar L.var F.var D.var L5.var Example: webuse invest2

23 Graphics [graph] twoway Other types of graphs: see help graph Example: sysuse auto scatter length weight

24 Graphics twoway scatter length weight /// lfit length weight /// qfit length weight graph export "Out/graphname.png" Stata graph examples hexamples/

25 Graphics Graphics must contain all relevant information. Labels, legend, axis titles, etc. Graphics are an important way to convey messages and require lots of work. See An Economist s Guide to Visualizing Data, Journal of Economic Perspectives, V.28(1), pp , 2014 (link)

26 Statistical tests Virtually infinite number of statistical tests Either stand-alone tests : Student t-test: ttest Or tests from regressions estimates Need to look for the commands that implement the test you are interested in.

27 Problem 3

28 Saved information and outputs Macros (numeric or string) Local macros cease to exist when the do-file terminates. local a = 2 local b = 3 di `a * `b di "`a and `b " Global macros cease to exist when the current Stata session terminates global pathtemp "/Data/Temp" di "$pathtemp " save "$pathtemp/mydata", replace

29 Saved information and outputs Also possible to use scalars and matrices. Stata commands: e-class and r-class (e for estimation commands and r for other commands) sysuse auto, clear summ price return list

30 Saved information and outputs reg price length ereturn list matrix list e(b) di _b[length]

31 Problem 4 Problem 5 (optional)

32 Exporting output Many different Stata commands depending on exact task outreg2 esttab, estout tabout Many are user-written commands (use Google, findit, ssc install).

33 Final project Freely available data French Government Open Data Penn World Tables Resources for Economists on the Internet FRED Economic Data World Bank Open Data