ECTS - University of Pisa
L'obiettivo del corso è quello di fornire agli studenti i principi fondamentali dell'econometria teorica e gli strumenti computazionali utilizzati nell'analisi empirica moderna on una particolare enfasi alla stima delle relazioni causali tra le variabili economiche. Applicazioni sia di microeconomia che di macroeconomia saranno considerate a tal fine.
The objective of the course is to give students with a thorough coverage of classical
econometric theory and the computational tools commonly used in modern empirical
analyses. Emphasis is on estimation of causal relations between economic
variables. Applications in the areas of microeconomics and macroeconomics will be considered.
Per l'accertamento delle conoscenze saranno svolte delle prove in itinere.
Ongoing assessment to monitor academic progress will be carried out.
Alla fine del corso gli studenti dovrebbero avere una comprensione critica delle idee alla base della teoria econometrica e dovrebbero essere in grado di applicare queste idee al mondo reale. Gli studenti inoltre acquisiranno familiarità con il software statistico R che sarà utilizzato durante tutto il corso.
By the end of the course, students should have a good and critical understanding
of the core ideas of econometric theory and they should be able to apply these ideas to
real world cases. Another expected learning outcome is that students be familiar with R,
the statistical software used throughout the course.
Alcune lezioni saranno dedicate alle appliazioni empiriche per le quali sarà usato il software statistico R.
Agli studenti non è richiesto di avere alcuna conoscenza di R o altra esperienza di programmazione, ma devono essere disposti a imparare.
Some lectures will be devoted to empirical applications and will require the use of a
statistical software. R is the statistical software for this course. Students are not required
to have any knowledge of R or other programming experience, but they must be willing
to learn.
Frequenza
La frequenza alle lezioni è consigliata, così come una preparazione per una piena partecipazione alla lezione. Gli studenti che hanno problemi possono fare domande durante la lezione o durante l'orario di ricevimento.
Imbrogli o altre forme di disonestà
Non ci saranno tolleranze nei confronti di qualsiasi tipo di imbroglio. Gli studenti che saranno trovati a copiare non supereranno l'esame e il loro comportamento sarà riportato agli uffici competenti.
Attendance
It is expected that all students attend the lectures, be up to date with their readings and
be prepared to participate fully in class. If you have problems mastering the material
covered in class, please ask questions in class or during office hours.
Cheating and other forms of dishonesty
I have no tolerance for cheating. I regard academic dishonesty as a very serious offense.
Students caught cheating during exams will fail the class and will be reported to the
appropriate officer of the college.
Durante le lezioni la partecipazione attiva degli studenti sarà valutata, così come la correttezza del loro comportamento nello svolgimento degli esami.
During the lecture the active participation of students will be evaluated, as well as their honesty during the exams.
Gli studenti devono essere familiari con i concetti base della probabilità, della statistica e dell'algebra lineare. Il corso include comunque un ripasso di statistica e probabilità. Gli studenti che non hanno familiarità con questi strumenti dovrebbero riverderli in dettaglio per loro conto.
Importante: Chi non avesso mai seguito corsi di introduzione all'econometria è fortemente consigliato di fare una lettura preliminare di un testo introduttivo tipo:
Gujarati, D.: Basic Econometrics. New York, McGraw-Hill, 2004.
Students need to be familiar with basic concepts in probability and statistics, as well as
linear algebra. The course includes a brief statistics and probability refresher just in case.
However, students lacking familiarity with this material should make time out of class
to review it in detail.
Important: If you have not taken an introductory econometrics
course, preparatory reading is strongly advised, for example:
Gujarati, D.: Basic Econometrics. New York, McGraw-Hill, 2004.
Lezioni frontali ed esercitazioni in laboratorio
Lectures and lab sessions
1. Interpolation with Ordinary Least Squares Method (OLS)
2. Simple and K-variables Linear Regression Model
(a) Basic assumptions, OLS estimation
(b) Statistical properties of the OLS estimator
(c) The Coefficient of determination R2
(d) Unbiased estimation of 2
(e) The normality assumption and distributions of quadratic forms (no proof)
(f) t-test and F-test for testing linear hypothesis (linear restrictions)
(g) The Gauss-Markov theorem
3. Further results on the regression model
(a) Functional forms: point elasticity, arch elasticity and semielasticity
(b) Dichotomous variables (dummy variables), multicollinearity, errors in variables
(c) Restricted Least Squares (RLS)
(d) Adding or deleting variables
4. Generalized Least Squares (GLS)
(a) Non spherical disturbances and OLS estimates, Generalized Least Squares
(GLS) and Feasible Generalized Least Squares (FGLS).
(b) Equivalence between GLS and OLS on transformed variables
(c) Heteroschedasticity (Estimation and White’s Test)
(d) Autocorrelation
5. Maximum Likelihood (ML) estimation
(a) Maximum Likelihood estimation (b) Wald test, Likelihood Ratio test and Lagrange Multiplier test
6. Endogeneity
(a) Endogenous regressors and inconsistency of OLS estimation
(b) Instrumental Variables (IV) and Two Stage Least Squares (TSLS)
(c) Control Function (CF) approach: test and estimate.
7. Panel Data Econometrics
8. Generalized Method of Moments (GMM) Approach
1. Interpolation with Ordinary Least Squares Method (OLS)
2. Simple and K-variables Linear Regression Model
(a) Basic assumptions, OLS estimation
(b) Statistical properties of the OLS estimator
(c) The Coefficient of determination R2
(d) Unbiased estimation of 2
(e) The normality assumption and distributions of quadratic forms (no proof)
(f) t-test and F-test for testing linear hypothesis (linear restrictions)
(g) The Gauss-Markov theorem
3. Further results on the regression model
(a) Functional forms: point elasticity, arch elasticity and semielasticity
(b) Dichotomous variables (dummy variables), multicollinearity, errors in variables
(c) Restricted Least Squares (RLS)
(d) Adding or deleting variables
4. Generalized Least Squares (GLS)
(a) Non spherical disturbances and OLS estimates, Generalized Least Squares
(GLS) and Feasible Generalized Least Squares (FGLS).
(b) Equivalence between GLS and OLS on transformed variables
(c) Heteroschedasticity (Estimation and White’s Test)
(d) Autocorrelation
5. Maximum Likelihood (ML) estimation
(a) Maximum Likelihood estimation (b) Wald test, Likelihood Ratio test and Lagrange Multiplier test
6. Endogeneity
(a) Endogenous regressors and inconsistency of OLS estimation
(b) Instrumental Variables (IV) and Two Stage Least Squares (TSLS)
(c) Control Function (CF) approach: test and estimate.
7. Panel Data Econometrics
8. Generalized Method of Moments (GMM) Approach
Principali libri di testo:
Agli studenti saranno anche forniti degli Handout.
Per R:
The main references for this courses are:
We will also provide students with some Handouts.
For R:
La valutazione sarà basata sull'esito di due esami scritti che verranno fatti in classe, una prova intermedia, tre homework e una prova finale. La prova intermedia si terrà durante una delle tre lezioni della sesta settimana di
lezioni (settimana che va da Lunedì 1 Aprile a Venerdì 5 Aprile).
I tre homework verteranno su analisi empiriche utilizzando il materiale discusso durante le lezioni. Gli homework saranno assegnati agli studenti periodicamente e dovranno essere consegnati all'inizio della lezione con le seguenti scadenze:
Handed out on Due back on
First homework Wednesday, March 20 Wednesday, March 27
Second homework Wednesday, April 17 Friday, May 3rd
Third homework Wednesday, May 15 Wednesday, May 22
Aver sostenuto la prova intermedia e aver consegnato gli homework sono condizione
necessaria e sufficiente per poter sostenere l’esame finale durante gli appelli del 2019—2020. Il peso di ciascuna prova nel determinare il voto complessivo é descritto nella seguente
tabella:
Prova intermedia 10%
Homework 30%
Esame finale 60%
Totale 100%
Students final grade depends on their performances on two written in-class exams, a
midterm and a comprehensive final, and three take-home exams. The midterm will be held in
class during on of the lecture of the course sixth week of instruction (the sixth week is
the week starting on April 1st, 2019 and ending on April 5th, 2019). The three take-home
exams ask students to perform empirical analysis using the material discussed in the
lectures. Take-home exams are made available to students periodically and are due back at the
beginning of class. Below is the schedule with which they are handed out and the day in
which they are due back:
Handed out on Due back on
First take-home Wednesday, March 13 Wednesday, March 20
Second take-home Wednesday, April 17 Friday, May 3rd
Third take-home Wednesday, May 15 Wednesday, May 22
Both the midterm and all take-home exams are compulsory and they will be valid until
the second semester of the academic year 2019—-2020. The final grade on this course
is a weighted average of homework, midterm, and comprehensive final according to the
following weights:
Take-home exams 30%
Midterm 10%
Comprehensive final 60%
Total 100%
Lecture times and locations
Wednesday: 12:15-13:45 (P2)
Thursday: 14:00-15:30 (Eco Mac)
Friday: 12:15-13:45(L2)
Office hours: Ragusa: Friday: 10:30-11:30 Parenti: Mon: 11:00-12:00
