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STATISTICAL COMPUTING

STATA

Program

Updated A.Y. 2018-2019

The aim of this course is to acquaint students with the basics of Stata (and its use for applied economics), and the basics of Pyhton (to work on webscraping tasks). The course will be mostly focused on micro-econometrics. Topics to be covered in the first semester (Gagliarducci) include: dataset management, descriptive statistics, graphics, loops, linear regression, instrumental variable models (IV), panel data models, event-study analysis, regression discontinuity (RD). Topics to be covered in the second semester (Rovigatti) include: scraping standard websites, extracting usable information and storing it in machine-readable format.

Course prerequisites and material
Students are supposed to be attending, or have attended, the course of Statistics and Econometrics, and having some knowledge of the concepts of lists and websites. Knowledge of html and programming best practices could be a plus, while being able to manage comma separated files is a prerequisite. In each class, we will compile a code on the scheduled topic (see below).


Final exam
PASS or FAIL. The final exam consists of a take-home assignment to be returned (via email) at least one week before the expected exam registration.

Suggested Text and Readings
- Stata documentation (any version)
- An Introduction to Modern Econometrics using Stata, C.F. Baum, 2006
- Statistics with Stata, by L.C. Hamilton, 2006
- Microeconometrics using Stata, by A.C. Cameron and P.K. Trivedi, 2009
- Mastering Metrics, by J. Angrist and S. Pischke, 2015
- An Introduction to Stata Programming, Christopher F. Baum, 2014
- Detailed guide for webscraping and data analysis with BeautifulSoup (with Python 3!):

All readings are available through the Biblioteca Vilfredo Pareto, located in the building B (http://economia.biblio.uniroma2.it/).

Additional material available at:
- https://github.com/michaelstepner/healthinequality-code/blob/master/code/readme.md
- http://www.stata.com/help.cgi?contents
- http://www.ats.ucla.edu/stat/stata/
- http://dss.princeton.edu/online_help/stats_packages/stata/
- http://web.missouri.edu/~kolenikovs/stata/Duke/commands.html
- http://www.mostlyharmlesseconometrics.com/blog/
- https://www.dataquest.io/blog/web-scraping-tutorial-python/
- http://python-guide-pt-br.readthedocs.io/en/latest/scenarios/scrape/
- https://scrapy.org/

Course Web Page
All the material for this class (syllabus, do files, announcements) will be posted on the course webpage.

Some of the data used in class are available at:
http://www.bancaditalia.it/statistiche/indcamp/bilfait/dismicro/annuale/stata