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93674 - Quantitative Economics And Public Policy

Academic Year 2024/2025

  • Docente: Lucio Picci
  • Credits: 8
  • SSD: SECS-P/02
  • Language: English
  • Teaching Mode: Traditional lectures
  • Campus: Forli
  • Corso: Second cycle degree programme (LM) in International Politics and Economics (cod. 5702)

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 learn some the foundations of statistics and, while doing that, to familiarize oneself with the Stata software and programming language.

Course contents

  • Introduction

Instructor's notes

  • Probability

  • Definitions; marginal and conditional probabilities

AFK: 5.1, 5.2, 5.3

  • Probability rules and Bayes' theorem

AFK: 5.4

  • Probability distributions

    • Binomial, Normal, student-t, chi-squared, and F-distribution

AFK: 6.1, 6.2.

  • Sampling distribution

AFK: 7.1, 7.2

  • Statistical inference: Estimation

AFK: 8.1, 8.2, 8.3, 8.4

  • 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

  • The model and estimation of the regression coefficients.

  • [SW] Chapter 4 (all).

  • Hypothesis tests and Confidence Intervals

  • [SW] Chapter 5 (all).

The linear multivariate regression model

  • The model and estimation of the regression coefficients.

  • [SW] Chapter 6 (all).

Test of hypothesis in the linear multivariate regression model

  • T-test and F-test.

  • Heteroskedasticity, testing, and robust standard error estimation.

  • [SW] Chapter 7 (all).                                                        

Assessing studies based on multiple regression

  • Internal and external validity

  • [SW] Chapter 9.1.

Readings/Bibliography

  • 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)

  • 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

Students will have to complete a project using Stata.

The final grade will be a weighted average of the result of the Stata Project and of the final exam (a written test), with respective weights 0.4 and 0.6.

A "pass" grade can be refused, and the final exam retaken, ONLY ONCE. The grade on the Stata project can't be refused.

The deadline for the Stata project is Friday 20 December 2024, at 11:59 pm.

Teaching tools

Stata software, for which we have a Campus licence

Office hours

See the website of Lucio Picci