Knowledge of micro and macroeconomics, statistical inference and linear regression are useful to fruitfully attend the course.
Compulsory prerequisites required (Propedeuticità) are published on the web site: https://lt-eco.unibg.it/it/node/119
This is an applied economics course which aims to introduce students to the empirical quantitative analysis of socio-economics topics, both at the micro and the macro level. More specifically, students will learn how to apply some statistical and econometric methods to relevant economic topics (such as the relationship between class size and student achievement, the wage returns to education). Furthermore, they will have the chance to test with real-world data the predictions of some of the theoretical models they have learned in other courses, such as micro and macroeconomics, industrial and innovation economics, labour economics, international economics, and economic policy.
Special attention will be devoted to the economic interpretation of the results and to policy implications.
At the end of the course, the student will be able to: 1) define an economic question that may be answered with a quantitative analysis; 2) identify and/or collect data to empirically answer this question; 3) use a regression analysis to answer the economic question; 4) discuss the potential policy implications of the main results.
7. Advanced topics in empirical economic analysis
7.1. Introduction to policy evaluation:
- Introduction to the “Ideal Experiment”
- Methodological challenges in policy evaluation
- Types of Experiments (laboratory, field and survey experiments)
- Regression Discontinuity Design
- Instrumental Variables
6.2. Introduction to the empirical analysis using Big Data
- Big data overview (drivers of big data, big data attributes, data structure)
- First Steps in Big Data: What is Machine Learning?
- Ridge regression and the Lasso
- Principal Component Analysis
- Limit and Pitfalls of Big Data
The course will mix frontal lectures with applied labs using Stata (one of the most used software worldwide for empirical analysis in many fields, including economics), in which students will have the chance to put "hands on" real data and test economic theories learned also in other economics courses
One take-home assignments (40% of the final mark) and final written test (60% of the final mark).
The take-home assignment will mainly consist of empirical applications using Stata.
The final test will consist of open questions on the topics covered by the course.
A minimum score of 18/30 in both the take-home assignment and the written test is required to pass the course.
The module 86061-MOD2 (3 CFU) is part of the 9 CFU course. Only students with Module 1 (86061-MOD1 - 6 CFU) in their study plan can choose this module.
Textbooks and reading lists:
The course does not systematically follow any particular textbook, but a large amount of material will be taken from the following textbooks:
- Angrist,JD and JS Pischke (2008) Mostly Harmless Econometrics. Princeton University Press
- Cunningham, S (2018) The Causal Identification Mixtape Yale University Press, Chapters
- Stock, J. and Watson, M. (2019), Introduction to Econometrics, Pearson.
- Cameron, C. and Trivedi, P. (2010), Microeconometrics using Stata: Revised edition, Stata Publication.
A detailed reading list will be communicated during the first lectures and posted on the Moodle page of the course.