40036 - Quantitative Methods for Social Research

Academic Year 2018/2019

  • Docente: Lucio Picci
  • Credits: 8
  • SSD: SECS-P/02
  • Language: English

Learning outcomes

This course provides students with an introduction to Statistics for the social sciences. Topics include basic mathematical tools used in social science modeling and statistics, theory of estimation and inference, regression analysis and differences of means. The course has an applied orientation. Examples draw heavily from political science, and will be analyzed using the software “Stata”. At the beginning of the course, it is assumed that all students are familiar with the topics that are typically treated in introductory statistics courses, and in particular with the main concpets of the theory of probability, estimation, and test of hypotheses. At the end of the course, the student is able to to apply the methods considered, using Stata, and to correctly interpret the results of his/her analyses.

Course contents

PLEASE NOTE: an always up-to-data syllabus is here: SYLLABUS
  • Introduction (Week 1 & 2)
    A bird's aye view of the main topics of the course
    On the history of econometric thought, see (in Italian)  Le verità sfuggenti dell'econometria, Lucio Picci, 2000.
    Quick review of the following themes: descriptive statistics; probability, random variables and probability distributions, sample analysis, estimation, test of hypotheses [SW] chapter 2, 3. [PP]: Chapter 1: All ; Chapter 2: All, with the exlusion of 2.10; Chapter 3: 3.1, 3.2, 3.4; Chapter 4: All, with the exclusion of 4.9 (Bayes' Theorem); Chapter 5. All, except 5.6 (Binomial distribution), 5.7 (Ipergeometric distribution) ; Chapter 6. All, except 6.6 (Sample distribution of a relative frequency); Chapter 7. All, except 7.6 (Interval estimation of a relative frequency); Chapter 8. All, except 8.4 (Test of hypothesis for a relative frequency) and 8.6 (Other cases)
    Stata Lab 1: Introduction to the Stata. Basic commands, data management, use of ".do files".
    Stata Lab 2: Exploratory data analysis.

  • The linear regression model with a single regressor (Week 3)
    Esimation of the coefficients and test of hypothesis on the regression coefficient. [SW] Chapter 4 & 5, all sections.
    Stata Lab 3: The linear regression model with a single regressor.

  • The linear multivariate regression model (Week 4)
    The model and estimation of the regression coefficients. [SW] Chapter 6 and 9 (see also: 17, 18.1, 18.2, 18.4 and 18.5).
    Stata Lab 4: The linear multivariate regression model, Part I.

  • Test of hypothesis in the linear multivariate regression model (Week 5)
    T-test and F-test. [SW] Chapter 7 (see also: 18.3)
    Stata Lab 5. The linear multivariate regression model, Part II.

  • The analysis of the linear regression model and heteroskedasticity (Week 6)
    Heteroskedasticity, testing, and robust standard error estimation. [SW] Chapter 7 all sections (see also: 18.6).
    Analysis of the regression model. [SW] Chapter 9.
    Stata Lab 6. Heteroskedasticity and GLS estimation.

  • Instrumental Variable Estimation (Week 7).
    Instrumental variable and 2SLS estimation. [SW] Chapter 12 (all sections) (also see: 18.7)
    Stata Lab 7. IV estimation

  • Panel data models (Week 8)
    Panel data structure.
    "Pooled" and "Fixed Effects" models. [SW] Chapter 10 (all sections).
    Stata Lab 8. Panel data models.


  • Discrete choice models (Week 9)
    [SW] Chapter 11 (all sections).
    Stata Lab 9. Estimation and analysis of Probit and Logit models.

  • Time series data and models. Review of topics (Week 10)
    Time series models. [SW] Chapter 14 (Sections 1-5; also, read Section 6).
    Stata Lab 10. Wrap-up of the course.

Readings/Bibliography

The main textbook is: Stock, James and Mark W. Watson. 2012. Introduction to Econometrics. 3rd edition. Pearson (indicated in what follows as [SW])
Also relevant is: Ray C. Fair. 2012. Predicting Presidential Elections and Other Things. 2nd. edition. Stanford University Press (The PDF file of the first edition is:  avialable online ). A copy is available at the Central library.

To review the topics whose knowledge is a prerequisite for this course, students may peruse any introductory text in statistics. Students who can read Italian may want to consider: Pacini, B. and Picci, L. 2001. Introduzione alla Statistica, Clueb (indicated in what follows as [PP]). A limited number of copies of this textbook may be borrowed from the library.

Teaching methods

20 hours of dedicated classes will be devoted to the use of the STATA software.

Assessment methods

The final grade will be based on one midterm exam (25%), one final exam (25%), and one  Stata project (read the instructions here ), which will be split into two parts (25% each). The first midterm exam will be taken after Week 5 of the course.

Links to further information

https://sites.google.com/view/quantitativemethods/

Office hours

See the website of Lucio Picci