Research Methods
A.Y. 2024/2025
Learning objectives
Aims and objectives: Module 1: Logic. The aim of this part of the course is to give students the preliminary elements of classical logic, and some basic information concerning informal logic and argument analysis.
Module 2: Statistics and Econometrics Statistics is dedicated to the introduction of basic elements of probability and inferential statistics. The objective is to provide students with the theoretical and practical notions for estimation and hypothesis testing.
The objective of Econometrics is to provide students with the basic principles of the econometric analysis. All the theoretical aspects of the econometric modelling will be treated jointly with interesting and modern empirical applications in order to motivate students and try to respond to real-world questions with specific numerical answers.
Module 2: Statistics and Econometrics Statistics is dedicated to the introduction of basic elements of probability and inferential statistics. The objective is to provide students with the theoretical and practical notions for estimation and hypothesis testing.
The objective of Econometrics is to provide students with the basic principles of the econometric analysis. All the theoretical aspects of the econometric modelling will be treated jointly with interesting and modern empirical applications in order to motivate students and try to respond to real-world questions with specific numerical answers.
Expected learning outcomes
Module 1: Logic. At the end of the course, the student will be familiar with the language of contemporary logic, and the main logical devices for the analysis and evaluation of reasoning, in science as well as in public communication. The student will know how to symbolize arguments expressed in ordinary language using the tools of propositional and predicative logic, and will be able to apply this knowledge to test the validity of formal arguments and to construct some elementary logical proofs. The student will also be able to reconstruct and evaluate informal arguments expressed in ordinary discourse applying a considerable set of argumentation schemes, and will be able to produce some good arguments to support a given claim.
Module 2. Statistics and Econometrics By the end of the module you will be able to:
Estimate a model using Least Squares.
Interpret the regression estimate and the computer output.
Apply diagnostics to check if the model is correctly specified and propose procedures to correct the miss-specification.
Run an independent analysis in an applied project.
Module 2. Statistics and Econometrics By the end of the module you will be able to:
Estimate a model using Least Squares.
Interpret the regression estimate and the computer output.
Apply diagnostics to check if the model is correctly specified and propose procedures to correct the miss-specification.
Run an independent analysis in an applied project.
Lesson period: First trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First trimester
Prerequisites for admission
This module is taught in English.
The module requires knowledge of differential calculus.
The module requires knowledge of differential calculus.
Assessment methods and Criteria
Assessment
Logics and Statistics & Econometrics are assessed separately, and the final mark is a weighted average of the mark of two parts, with weights 1/4 for Logics and 3/4 for Statistics and Econometrics (reflecting the hours allocated to each part).
Logic is assessed by means of a 90 minutes written exam, composed of two parts:
Part A. Formal logic test: semi-structured test with multiple choice questions and open questions (45 minutes).
Part B. Argument Analysis: open questions (45 minutes)
Evaluation parameters: knowledge of logical and argumentative tools, ability in reconstructing and evaluating formal and informal arguments, capacity of constructing simple proofs and arguments.
Statistics and Econometrics is assessed by means of a 90 minutes written exam only (assessing both parts jointly). Only for the December 2024 Assessment, it is possible to complement the Statistics and Econometrics exam with a Mini-Project.
Logics and Statistics & Econometrics are assessed separately, and the final mark is a weighted average of the mark of two parts, with weights 1/4 for Logics and 3/4 for Statistics and Econometrics (reflecting the hours allocated to each part).
Logic is assessed by means of a 90 minutes written exam, composed of two parts:
Part A. Formal logic test: semi-structured test with multiple choice questions and open questions (45 minutes).
Part B. Argument Analysis: open questions (45 minutes)
Evaluation parameters: knowledge of logical and argumentative tools, ability in reconstructing and evaluating formal and informal arguments, capacity of constructing simple proofs and arguments.
Statistics and Econometrics is assessed by means of a 90 minutes written exam only (assessing both parts jointly). Only for the December 2024 Assessment, it is possible to complement the Statistics and Econometrics exam with a Mini-Project.
Modulo 1
Course syllabus
LOGICS
· What is contemporary logic
· Propositional logic: the language
· Propositional logic: the rules
· Predicate logic: the language
· Predicate logic: the rules
· Logic and the analysis of public discourse
STATISTICS
· Preliminary mathematics and introduction to the probability model;
· Random variables and distributions;
· Mathematical expectations;
· Some useful distributions;
· Asymptotic / large sample distribution theory;
· Sampling distributions;
· Point estimation;
· Maximum likelihood estimation;
· Interval estimation;
· Hypothesis testing.
· What is contemporary logic
· Propositional logic: the language
· Propositional logic: the rules
· Predicate logic: the language
· Predicate logic: the rules
· Logic and the analysis of public discourse
STATISTICS
· Preliminary mathematics and introduction to the probability model;
· Random variables and distributions;
· Mathematical expectations;
· Some useful distributions;
· Asymptotic / large sample distribution theory;
· Sampling distributions;
· Point estimation;
· Maximum likelihood estimation;
· Interval estimation;
· Hypothesis testing.
Teaching methods
Logic: 10 two-hour lectures
Statistics: 10 two-hour lectures
Statistics: 10 two-hour lectures
Teaching Resources
[Reference Materials]:
LOGICS
Suggested Handbook: forallx:Cambridge, P.D. Magnus, University at Albany, State University of New York. Modified for the Cambridge Course by: Tim Button, University of Cambridge (available online).
At the end of the course, the lecturer's notes will be placed at students' disposal.
STATISTICS
Most introductory statistics book provide a useful and detailed reference to review the material of these lectures. For example,
∙ Miller, I. and M. Miller, 2004. John E. Freund's mathematical statistics with applications, 7th edition, Pearson Prentice-Hall.
Many introductory econometrics texts also contain useful reviews of the relevant statistical concepts. For example,
∙ Wooldridge, J., 2003. Introductory econometrics, 2nd ed., South Western College Publishing, Appendix B and C.
LOGICS
Suggested Handbook: forallx:Cambridge, P.D. Magnus, University at Albany, State University of New York. Modified for the Cambridge Course by: Tim Button, University of Cambridge (available online).
At the end of the course, the lecturer's notes will be placed at students' disposal.
STATISTICS
Most introductory statistics book provide a useful and detailed reference to review the material of these lectures. For example,
∙ Miller, I. and M. Miller, 2004. John E. Freund's mathematical statistics with applications, 7th edition, Pearson Prentice-Hall.
Many introductory econometrics texts also contain useful reviews of the relevant statistical concepts. For example,
∙ Wooldridge, J., 2003. Introductory econometrics, 2nd ed., South Western College Publishing, Appendix B and C.
Modulo 2
Course syllabus
[Programme]:
ECONOMETRICS
· The nature of econometrics and economic data
· Regression analysis with cross sectional data
· Linear Regression with One Regressor
· Linear Regression with Multiple Regressors
· Hypothesis Testing
· Large samples
· Further issues in the linear regression model
· Instrumental Variable Regression (IV-TSLS)
· Regression with a Binary Dependent Variable
· Regression with pooled cross sections
· Regression with panel data
· Regression with time series data
ECONOMETRICS
· The nature of econometrics and economic data
· Regression analysis with cross sectional data
· Linear Regression with One Regressor
· Linear Regression with Multiple Regressors
· Hypothesis Testing
· Large samples
· Further issues in the linear regression model
· Instrumental Variable Regression (IV-TSLS)
· Regression with a Binary Dependent Variable
· Regression with pooled cross sections
· Regression with panel data
· Regression with time series data
Teaching methods
20 two-hours lectures
Teaching Resources
The reference book is:
∙ Wooldridge, J., 2003. Introductory econometrics, 2nd ed., South Western College Publishing.
∙ Wooldridge, J., 2003. Introductory econometrics, 2nd ed., South Western College Publishing.
Modulo 1
SPS/04 - POLITICAL SCIENCE - University credits: 6
Lessons: 40 hours
Professors:
D'Agostini Franca, Iacone Fabrizio
Modulo 2
SECS-P/05 - ECONOMETRICS - University credits: 6
Lessons: 40 hours
Professor:
Iacone Fabrizio
Educational website(s)
Professor(s)
Reception:
Thursday, 11AM to 1PM. Please email me to arrange an appointment
Stanza 4 (Second floor)