- Docente: Lucio Picci
- Credits: 8
- SSD: ECON-02/A
- Language: English
- Teaching Mode: In-person learning (entirely or partially)
- Campus: Forli
- Corso: Second cycle degree programme (LM) in International Politics and Economics (cod. 6763)
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from Sep 29, 2026 to Dec 15, 2026
Learning outcomes
The course provides an overview of some of the most widely used quantitative methods for social sciences and economics, with an emphasis on regression analysis. The general linear regression model is considered at length, together with some of its incarnations and extensions. These include: the instrumental variables method, models for discrete random variables, models for panel data, and models for time series data. The course has an applied orientation. Examples draw heavily from the political sciences, and are analyzed using the R programming language. At the end of the course, diligent students will be able to apply the methods considered, using R, and to correctly interpret the results of their analyses.
Course contents
The purpose of this course is to consolidate students' knowledge of inferential statistics, with particular emphasis on the multiple linear regression model, while also developing familiarity with the Stata statistical software and its programming language.
The material covered in the first part of the course, which focuses on inferential statistics, will be reviewed relatively quickly in order to reinforce the concepts already introduced in the Crash Course in Statistics.
Course contents
- Introduction
Instructor's notes
- Probability
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Definitions; marginal and conditional probabilities
AFK: 5.1, 5.2, 5.3
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Probability rules and Bayes' theorem
AFK: 5.4
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Probability distributions
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Binomial, Normal, student-t, chi-squared, and F-distribution
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AFK: 6.1, 6.2.
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Sampling distribution
AFK: 7.1, 7.2
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Statistical inference: Estimation
AFK: 8.1, 8.2, 8.3, 8.4
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Statistical inference: Test of hypothesis
(on the mean, the relative frequency, and on statistical independence -Chi- square test)
AFK: 9.1, 9.2, 9.3, 9.4, 9.5, 9.6.
The linear bivariate regression model
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The model and estimation of the regression coefficients.
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[SW] Chapter 4 (all).
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Hypothesis tests and Confidence Intervals
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[SW] Chapter 5 (all).
The linear multivariate regression model
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The model and estimation of the regression coefficients.
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[SW] Chapter 6 (all).
Test of hypothesis in the linear multivariate regression model
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T-test and F-test.
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Heteroskedasticity, testing, and robust standard error estimation.
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[SW] Chapter 7 (all).
Assessing studies based on multiple regression
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Internal and external validity
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[SW] Chapter 9.1.
Readings/Bibliography
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Agresti, Alan; Christine Franklin and Bernhard Klingenberg. "Statistics. The Art and Science of Learning from Data". Fourth Edition. Pearson, 2017. (indicated in the syllabus as: AFK)
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Stock, James and Mark W. Watson. 2020. Introduction to Econometrics. 4th edition. Pearson (indicated in the syllabus as SW).
Teaching methods
Classes in presence
Assessment methods
The final examination is divided into two parts:
- A written exam, in which students will solve quantitative problems and answer a small number of open-ended questions.
- A data analysis exercise using Stata, in which students will analyze a dataset and answer questions based on their results.
NB: Internet access will be disabled during the examination. Students who have relied excessively on AI during the course and, as a result, have not developed autonomous problem-solving and Stata coding skills will find it extremely difficult to pass the exam.
Teaching tools
Stata software, for which we have a Campus licence
Office hours
See the website of Lucio Picci