Syllabus
EN
IT
Learning Objectives
LEARNING OUTCOMES: The aim of this course is to acquaint students with the basics of R, MATLAB and Stata, and their usage in applied economics, and the basics of Python, for webscraping tasks.
KNOWLEDGE AND UNDERSTANDING: the final goal is to gain knowledge of the analytical tools to understand the most common micro and macro-econometric models, even in a research context.
APPLYING KNOWLEDGE AND UNDERSTANDING: develop the ability to deal with the empirical analysis of micro and macro models in a systematic way.
MAKING JUDGEMENTS: acquire the computational and methodological tools to analyze the choices of the national and european policy makers.
COMMUNICATION SKILLS: students must be able to deliver the emprical results, in a rigorous way, to an (expert or non-expert) audience.
LEARNING SKILLS: students can undertake the in-depth study of the considered, or other, softwares.
KNOWLEDGE AND UNDERSTANDING: the final goal is to gain knowledge of the analytical tools to understand the most common micro and macro-econometric models, even in a research context.
APPLYING KNOWLEDGE AND UNDERSTANDING: develop the ability to deal with the empirical analysis of micro and macro models in a systematic way.
MAKING JUDGEMENTS: acquire the computational and methodological tools to analyze the choices of the national and european policy makers.
COMMUNICATION SKILLS: students must be able to deliver the emprical results, in a rigorous way, to an (expert or non-expert) audience.
LEARNING SKILLS: students can undertake the in-depth study of the considered, or other, softwares.
FEDERICO BELOTTI
Prerequisites
Students are supposed to be attending, or have attended, the course of Statistics and Econometrics.
Program
R and MATLAB: data import and export, plotting commands, descriptive statistics, functions for random variables, the likelihood approach, the regression model, time series models.
Stata: mechanics (do files, data and datasets), programming (macros, scalar, matrice, branching & looping), descriptives (graphs and tables) estimation of the linear regression model using least-squares and instrumental variables approaches.
Python: scraping standard websites, extracting usable information and storing it in machine-readable format.
Stata: mechanics (do files, data and datasets), programming (macros, scalar, matrice, branching & looping), descriptives (graphs and tables) estimation of the linear regression model using least-squares and instrumental variables approaches.
Python: scraping standard websites, extracting usable information and storing it in machine-readable format.
Books
All the material (slides, data files, scripts) will be posted on the course webpage.
Bibliography
Suggested readings
- Bourke (2018). "Computer Science I", available at
https://cse.unl.edu/~cbourke/ComputerScienceOne.pdf
First module
- Davies (2016). "The book of R". No starch press
- Cho, Martinez (2014). Statistics in MATLAB: A Primer. Chapman and Hall/CRC
Second module
- Christopher F. Baum (2016), An Introduction to Stata Programming, Second Edition, Stata Press
- Detailed guide for webscraping and data analysis with BeautifulSoup (with Python 3!)
- 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
- Bourke (2018). "Computer Science I", available at
https://cse.unl.edu/~cbourke/ComputerScienceOne.pdf
First module
- Davies (2016). "The book of R". No starch press
- Cho, Martinez (2014). Statistics in MATLAB: A Primer. Chapman and Hall/CRC
Second module
- Christopher F. Baum (2016), An Introduction to Stata Programming, Second Edition, Stata Press
- Detailed guide for webscraping and data analysis with BeautifulSoup (with Python 3!)
- 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
Teaching methods
Unless otherwise stated, according to the evolution of the COVID19 emergency, lectures will be held in class.
Exam Rules
Intermediate tests will be proposed during the course. Students having a positive grade in all the intermediate tests will have a final 'pass' grade.
The intermediate test for the first module (R/Matlab) will be held in October 25th.
The final exam consists of a practical test about the different softwares.
During the tests, students will be asked to perform an empirical analysis using one or more softwares, also providing a comment about the steps of the analyses and the final result. Students should demonstrate the knowledge of the softwares and the ability to use them. They should also be able to illustrate the steps of the analysis and to interpret the results.
The intermediate test for the first module (R/Matlab) will be held in October 25th.
The final exam consists of a practical test about the different softwares.
During the tests, students will be asked to perform an empirical analysis using one or more softwares, also providing a comment about the steps of the analyses and the final result. Students should demonstrate the knowledge of the softwares and the ability to use them. They should also be able to illustrate the steps of the analysis and to interpret the results.
ANTONIO PARISI
Program
R and MATLAB: data import and export, plotting commands, descriptive statistics, functions for random variables, the likelihood approach, the regression model, time series models.
Stata: mechanics (do files, data and datasets), programming (macros, scalar, matrice, branching & looping), descriptives (graphs and tables) estimation of the linear regression model using least-squares and instrumental variables approaches.
Python: scraping standard websites, extracting usable information and storing it in machine-readable format.
Stata: mechanics (do files, data and datasets), programming (macros, scalar, matrice, branching & looping), descriptives (graphs and tables) estimation of the linear regression model using least-squares and instrumental variables approaches.
Python: scraping standard websites, extracting usable information and storing it in machine-readable format.
Books
- Bourke (2018). "Computer Science I", available at
https://cse.unl.edu/~cbourke/ComputerScienceOne.pdf
- Davies (2016). "The book of R". No starch press
- Cho, Martinez (2014). Statistics in MATLAB: A Primer. Chapman and Hall/CRC
- Christopher F. Baum (2016), An Introduction to Stata Programming, Second Edition, Stata Press
- Detailed guide for webscraping and data analysis with BeautifulSoup (with Python 3!)
- 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
https://cse.unl.edu/~cbourke/ComputerScienceOne.pdf
- Davies (2016). "The book of R". No starch press
- Cho, Martinez (2014). Statistics in MATLAB: A Primer. Chapman and Hall/CRC
- Christopher F. Baum (2016), An Introduction to Stata Programming, Second Edition, Stata Press
- Detailed guide for webscraping and data analysis with BeautifulSoup (with Python 3!)
- 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
Exam Rules
For each part of the course (R/Matlab, Stata, and Python), one or more intermediate tests will be held to verify the students' achievements. A positive evaluation for R/Matlab tests (or Stata or Python) will guarantee an exemption for that part of the course from the final exam. The exemption will remain valid for the entire academic year. The rules to be awarded a positive evaluation are different for each part and are specified in the related "teaching material" section. Notice that, to earn a final pass grade for the course, students must earn a positive evaluation in the three parts. Otherwise, they will have to take a final exam, in presence, consisting of a practical test for all the parts of the course in which they failed.
Non-attending students will sit the final exam for all the three parts of the course.
Please notice that, in any case, students must book the exam (through Delphi) and be present on the exam date.
Non-attending students will sit the final exam for all the three parts of the course.
Please notice that, in any case, students must book the exam (through Delphi) and be present on the exam date.