77953 - DATA ANALYSIS

Scheda insegnamento

  • Docente Sara Capacci

  • Crediti formativi 8

  • SSD SECS-S/03

  • Modalità didattica Convenzionale - Lezioni in presenza

  • Lingua di insegnamento Inglese

  • Campus di Forli

  • Corso Laurea Magistrale in International politics and economics (cod. 5702)

  • Orario delle lezioni dal 06/03/2023 al 24/05/2023

Anno Accademico 2022/2023

Conoscenze e abilità da conseguire

The course provides an overview of the foundations of data analysis for social sciences and economics, with an emphasis on regression analysis. The general linear regression model is considered, together with some of its extensions. The course is applied in nature and is designed to provide students with a working knowledge of methods. Examples and case studies are analysed using a statistical software. At the end of the course students are able to apply the methods considered, to critically interpret empirical results and to effectively report the analysis to non-statisticians.

Contenuti

  • Recap on elasticity and marginal effects
  • Non-linear relations using the multiple linear regression: Log transformations (log-log, log-linear and linear-log models)
  • Regression models for limited dependent variables: linear probability models, probit and logit models
  • Endogeneity and the IV approach
  • Introduction to Panel data models (fixed-effects)

Testi/Bibliografia

Stock, J.H. and Watson, M.W. "Introduction to Econometrics", 4th edition. Published by Pearson.

Metodi didattici

During the course theoretical and practical sessions will be held. During practical sessions empirical knowledge of the proposed methods will be reached through real-world case studies performed using Stata.

(Stata is available in all the computer labs in the Campus. Moreover, a Campus licence of Stata is available to all students enrolled in the course)

Assignments will be proposed regularly. They will serve to reinforce class concepts and get familiarity with the software. Students are allowed and encouraged to work together on home assignments. However, a separate write-up is expected from each student, in his/her own words. Assignments will not be graded; solutions will be provided for self-assessment.

Modalità di verifica e valutazione dell'apprendimento

The course has a required cumulative final examination. You must take, and pass, the final examination to receive a passing grade in the course. The final exam will be a written test (computer based). Students are required to enrol using Almaesami.

The test contains three sections: 1) multiple choice/short-answers section (35% of Exam Score, 6 questions); 2) free response section on regression outputs (20% of Exam Score, 2 questions); 3) practical section with Stata (45% of Exam Score, 5 questions). The test duration is 75 minutes. (Please notice that the test structure might change; any modifications will be communicated in class).

The grading system is on a 0-30 range, the following grid applies:

  • <18 failed
  • 18-23 sufficient
  • 24-27 good
  • 28-30 very good
  • 30 cum laude honors

Strumenti a supporto della didattica

The UNIBO e-learning platform (VIRTUALE) will be used to share teaching materials and to assign periodical home assignments to students. The teaching material is particularly rich, and it is composed of:

  • Slides/Lecture notes: summarising theoretical concepts shown in class
  • Stata datasets (named “Example 1”, “Example 2”, etc) used to formulate examples described in the slides (students can use these datasets to replicate examples discussed in class)
  • Do files, lecture notes and Stata datasets: with these tools students are able to follow the practical sessions step by step and to completely replicate them at home.
  • Stata Assignments and Solutions which will be regularly proposed to students
  • Miscellanea: exercises, focus notes, sample tests will be uploaded when needed

Orario di ricevimento

Consulta il sito web di Sara Capacci