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CODING FOR ECONOMIC APPLICATIONS

1st Module

Syllabus

EN IT

Learning Objectives

LEARNING OUTCOMES: The aim of this course is to acquaint students with the basics of R, MATLAB, Stata and Python and their usage in applied economics.

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.

Prerequisites

There are no prerequisites for this course.

Program

During the first module, R and MATLAB (24 hours) are introduced and, in particular, data import and export, plotting commands, descriptive statistics, functions for random variables and the likelihood approach.

During the first module, Stata and Python (24 hours) are introduced.
Stata: mechanics (do files, data and datasets), programming (macros, scalar, matrices, branching and looping), descriptives (graphs and tables), maps.

Python: python essentials, functions and objects, data structures, data visualization, some applications of python on economic models.

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
- Microeconometrics Using Stata, A. C. Cameron and P. K. Trivedi, Stata press
- Python for Everybody, Exploring Data using Python 3, by Charles Severance
- Stata documentation (any version)
- Christopher F. Baum (2016), An Introduction to Stata Programming, Second Edition, Stata Press
- An Introduction to Modern Econometrics using Stata, C.F. Baum, 2006
- Statistics with Stata, by L.C. Hamilton, 2006
- Mastering Metrics, by J. Angrist and S. Pischke, 2015
- An Introduction to Stata Programming, Christopher F. Baum, 2014

Teaching methods

Lectures are held in the computer lab twice a week. Students can use their own devices.
Teaching materials (slides and scripts) will be provided before and after the lectures.

Exam Rules

The exam consists of a practical test, in presence, on the three parts of the course: R/Matlab, Stata and Python. It is necessary to obtain a positive evaluation for all the three parts to pass the exam. The result of the exam is a single mark (pass or fail), and it is immediately communicated to the student. Partially positive results don't give any exemption on single parts of the course.
Students who withdraw or fail an exam may take the exam again in the same exam session.
At the end of each module, an intermediate test will be held to verify the students' achievements, respectively about the R/Matlab and STATA parts. A positive evaluation for these tests will guarantee an exemption for the same parts from the final exam. The exemption will remain valid for the entire academic year.
Students must book the exam through Delphi and be present on the exam date.
Criteria for the formulation of the evaluation
- Fail: significant deficiencies in knowledge and understanding of the topics; frequent difficulties in writings codes.
- Pass: at least a sufficient knowledge of the topics and autonomy in writing codes.

Attendance Rules

Attendance is not compulsory, but strongly recommended.