ID:
86078-MOD1
Dettaglio:
SSD: Economic Policy
Duration: 48
CFU: 6
Located in:
BERGAMO
Url:
ECONOMICS - 86-270/PERCORSO COMUNE Year: 3
ECONOMICS - 86-270/ECONOMIA APPLICATA Year: 3
Year:
2025
At the end of the course, the students will be
able to 1) define an economic question that
may be answered with a quantitative
analysis; 2) understand the problem of
identification of an economic parameter
relevant to answer the question; 3) explore a
database to detect patterns, outliers and
statistical properties; 4) program a basic code
in Stata to explore data and run a linear
estimator to address an economic question;
5) read and interpret the results of a
regression analysis; 6) Conduct a policy
evaluation using a research design; 7)
understand the logic of empirical analysis
using Big Data
Knowledge of basic 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.
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.
Two take-home assignments (60% of the final
mark) and final written test (40% of the final
mark).
The take-home assignments 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 course will cover the following topics:
1. Introduction to modern empirical
economics; the model of potential outcomes;
the concept of causality, counterfactual, and
self-selection; introduction to properties of sampling
2. Getting familiar with Stata: data cleaning,
variables creation and descriptive statistics.
3. OLS regression. Mean independence.
Algebraic and asymptotic properties of OLS.
The regression anatomy and the omitted
variable formula
4. Regression talk: Economic and statistical
significance; effect size and power analysis;
non-linearity and interaction terms; DAG and
choice of controls
5. Validity. The Campbell Approach. Threats
to internal validity. External Validity
6. Limited Dependent Variable. Linear
Probability Model. Logit and Probit. MLE.
Analyzing prediction and the ROC
7. Panel Data. Notation. Merging datasets.
Fixed Effects estimator