EN
IT
Obiettivi Formativi
OBIETTIVI FORMATIVI: introdurre gli studenti all'uso dei software R, MATLAB, Stata e Python per l'analisi empirica in campo economico.
CONOSCENZA E CAPACITÀ DI COMPRENSIONE: l'obiettivo finale è quello di favorire la comprensione del funzionamento dei software al fine di implementare un approccio analitico per l'analisi delle questione economiche, anche in un contesto di ricerca.
CAPACITÀ DI APPLICARE CONOSCENZA E COMPRENSIONE: affrontare in maniera organica l'analisi empirica di dati micro e macroeconomici.
AUTONOMIA DI GIUDIZIO: acquisire gli strumenti computazionali e metodologici per analizzare l'operato dei policy maker nazionali ed europei.
ABILITÀ COMUNICATIVE: saper presentare risultati empirici in maniera rigorosa a interlocutori specialisti e non specialisti.
CAPACITÀ DI APPRENDIMENTO: gli studenti potranno intraprendere lo studio approfondito dei software considerati, o di ulteriori software.
Learning Objectives
LEARNING OUTCOMES: The aim of this course is to acquaint students with the basics of R, MATLAB, Stata and Python and their usage in applied economics.
KNOWLEDGE AND UNDERSTANDING: the final goal is to gain knowledge of the analytical tools to understand the most common micro and macro-econometric models, even in a research context.
APPLYING KNOWLEDGE AND UNDERSTANDING: develop the ability to deal with the empirical analysis of micro and macro models in a systematic way.
MAKING JUDGEMENTS: acquire the computational and methodological tools to analyze the choices of the national and european policy makers.
COMMUNICATION SKILLS: students must be able to deliver the emprical results, in a rigorous way, to an (expert or non-expert) audience.
LEARNING SKILLS: students can undertake the in-depth study of the considered, or other, softwares.
ANTONIO PARISI
Prerequisiti
Non ci sono prerequisiti per frenquare il corso.
Prerequisites
There are no prerequisites for students to attend this course.
Programma
Nel primo modulo, vengono introdotti R e MATLAB (24 ore), considerando, in particolare, l'importazione ed esportazione dei dati, i comandi grafici, le statistiche descrittive, le funzioni per variabili casuali, la stima con il metodo della verosimiglianza e il modello di regressione.
Program
During the first module, R and MATLAB (24 hours) are introduced and, in particular, data import and export, plotting commands, descriptive statistics, functions for random variables, the likelihood approach and the regression model.
Testi Adottati
I materiali delle lezioni (slides, file di dati, script) saranno disponibili sul sito del corso.
Books
All the material (slides, data files, scripts) will be posted on the course webpage.
Bibliografia
Letture suggerite
- Bourke (2018). "Computer Science I", disponibile al link
https://cse.unl.edu/~cbourke/ComputerScienceOne.pdf
- Davies (2016). "The book of R". No starch press
- Cho, Martinez (2014). "Statistics in MATLAB: A Primer". Chapman and Hall/CRC
Bibliography
Suggested readings
- Bourke (2018). "Computer Science I", available at
https://cse.unl.edu/~cbourke/ComputerScienceOne.pdf
- Davies (2016). "The book of R". No starch press
- Cho, Martinez (2014). Statistics in MATLAB: A Primer. Chapman and Hall/CRC
Modalità di svolgimento
Le lezioni si tengono nel laboratorio informatico due volte la settimana. Gli studenti potranno utilizzare i propri dispositivi.
Verranno pubblicati dei materiali didattici (slide e script) prima e dopo le lezioni.
Teaching methods
Lectures are held in the computer lab twice a week. Students can use their own devices.
Teaching materials (slides and scripts) will be provided before and after the lectures.
Regolamento Esame
L'esame consiste in una prova pratica, in presenza, sulle tre parti del corso: R/Matlab, Stata e Python. Bisogna ottenere una valutazione positiva in tutte le parti per superare l'esame. L'esito dell'esame è una singola idoneità, che viene comunicata immediatamente. Risultati parzialmente positivi non esonerano dalle singole parti del corso in nessun caso.
Gli studenti che si ritirano, o che non risultino idonei, possono presentarsi ai successivi appelli d'esame della stessa sessione.
Alla fine del primo modulo, si terrà una verifica intermedia sulla parte relativa ad R e Matlab. La valutazione positiva a questa prova esonera lo studente per la stessa parte dall'esame finale. L'esonero rimarrà valido per l'intero anno accademico.
Gli studenti dovranno prenotare l'esame tramite Delphi ed essere presenti al giorno dell'esame.
Criteri per la formulazione del giudizio
- Non idoneo: importanti carenze nella conoscenza e comprensione degli argomenti; frequenti difficoltà nella stesura dei codici.
- Idoneo: conoscenza degli argomenti e autonomia nello scrivere codici almeno sufficiente.
Exam Rules
The exam consists of a practical test, in presence, on the three parts of the course: R/Matlab, Stata and Python. It is necessary to obtain a positive evaluation for all the three parts to pass the exam. The result of the exam is a single mark (pass or fail), and it is immediately communicated to the student. Partially positive results doesn't give any exemption on single parts of the course.
Students who withdraw or fail an exam may take the exam again in the same exam session.
At the end of the first module, an intermediate test will be held to verify the students' achievements about the R/Matlab part. A positive evaluation for this test will guarantee an exemption for the same part from the final exam. The exemption will remain valid for the entire academic year.
Students must book the exam through Delphi and be present on the exam date.
Criteria for the formulation of the evaluation
- Fail: significant deficiencies in knowledge and understanding of the topics; frequent difficulties in writings codes.
- Pass: at least a sufficient knowledge of the topics and autonomy in writing codes.
FRANCESCA MARAZZI
Prerequisiti
Non ci sono prerequisiti per questo corso.
Prerequisites
There are no prerequisites for this course.
Programma
Nel secondo modulo, vengono introdotti Stata e Python (24 ore).
Stata: elementi di base (do files, dati e datasets), programmazione (macro, scalari, matrici e cicli), statistiche descrittive (grafici e tabelle), stima ed interpretazione del modello di regressione lineare.
Python: elementi di base, funzioni e oggetti, strutture dati, visualizzazione dei dati, applicazioni di python su modelli economici.
Program
During the second module, Stata and Python (24 hours) are introduced.
Stata: mechanics (do files, data and datasets), programming (macros, scalar, matrices,
branching and looping), descriptives (graphs and tables), estimation and interpretation of
the linear regression model.
Python: python essentials, functions and objects, data structures, data visualization, some
applications of python on economic models.
Bibliografia
Letture suggerite
- Bourke (2018). "Computer Science I", disponibile al link
https://cse.unl.edu/~cbourke/ComputerScienceOne.pdf
- Microeconometrics using Stata, by A.C. Cameron and P.K. Trivedi, 2009
- Python for Everybody, Exploring Data using Python 3, by Charles Severance
- Stata documentation (any version)
- Christopher F. Baum (2016), An Introduction to Stata Programming, Second Edition, Stata Press
- An Introduction to Modern Econometrics using Stata, C.F. Baum, 2006
- Statistics with Stata, by L.C. Hamilton, 2006
- Mastering Metrics, by J. Angrist and S. Pischke, 2015
- An Introduction to Stata Programming, Christopher F. Baum, 2014
Bibliography
Suggested readings
- Bourke (2018). "Computer Science I", available at
https://cse.unl.edu/~cbourke/ComputerScienceOne.pdf
- Microeconometrics Using Stata, A. C. Cameron and P. K. Trivedi, Stata press
- Python for Everybody, Exploring Data using Python 3, by Charles Severance
- Stata documentation (any version)
- Christopher F. Baum (2016), An Introduction to Stata Programming, Second Edition, Stata Press
- An Introduction to Modern Econometrics using Stata, C.F. Baum, 2006
- Statistics with Stata, by L.C. Hamilton, 2006
- Mastering Metrics, by J. Angrist and S. Pischke, 2015
- An Introduction to Stata Programming, Christopher F. Baum, 2014
EN
IT
Obiettivi Formativi
OBIETTIVI FORMATIVI: introdurre gli studenti all'uso dei software R, MATLAB, Stata e Python per l'analisi empirica in campo economico.
CONOSCENZA E CAPACITÀ DI COMPRENSIONE: l'obiettivo finale è quello di favorire la comprensione del funzionamento dei software al fine di implementare un approccio analitico per l'analisi delle questione economiche, anche in un contesto di ricerca.
CAPACITÀ DI APPLICARE CONOSCENZA E COMPRENSIONE: affrontare in maniera organica l'analisi empirica di dati micro e macroeconomici.
AUTONOMIA DI GIUDIZIO: acquisire gli strumenti computazionali e metodologici per analizzare l'operato dei policy maker nazionali ed europei.
ABILITÀ COMUNICATIVE: saper presentare risultati empirici in maniera rigorosa a interlocutori specialisti e non specialisti.
CAPACITÀ DI APPRENDIMENTO: gli studenti potranno intraprendere lo studio approfondito dei software considerati, o di ulteriori software.
Learning Objectives
LEARNING OUTCOMES: The aim of this course is to acquaint students with the basics of R, MATLAB, Stata and Python and their usage in applied economics.
KNOWLEDGE AND UNDERSTANDING: the final goal is to gain knowledge of the analytical tools to understand the most common micro and macro-econometric models, even in a research context.
APPLYING KNOWLEDGE AND UNDERSTANDING: develop the ability to deal with the empirical analysis of micro and macro models in a systematic way.
MAKING JUDGEMENTS: acquire the computational and methodological tools to analyze the choices of the national and european policy makers.
COMMUNICATION SKILLS: students must be able to deliver the emprical results, in a rigorous way, to an (expert or non-expert) audience.
LEARNING SKILLS: students can undertake the in-depth study of the considered, or other, softwares.
ANTONIO PARISI
Programma
Nel primo modulo, vengono introdotti R e MATLAB (24 ore), considerando, in particolare, l'importazione ed esportazione dei dati, i comandi grafici, le statistiche descrittive, le funzioni per variabili casuali, la stima con il metodo della verosimiglianza e il modello di regressione.
Program
During the first module, R and MATLAB (24 hours) are introduced and, in particular, data import and export, plotting commands, descriptive statistics, functions for random variables, the likelihood approach and the regression model.
Testi Adottati
I materiali delle lezioni (slides, file di dati, script) saranno disponibili sul sito del corso.
Books
All the material (slides, data files, scripts) will be posted on the course webpage.
Bibliografia
Letture suggerite
- Bourke (2018). "Computer Science I", disponibile al link
https://cse.unl.edu/~cbourke/ComputerScienceOne.pdf
- Davies (2016). "The book of R". No starch press
- Cho, Martinez (2014). "Statistics in MATLAB: A Primer". Chapman and Hall/CRC
Bibliography
Suggested readings
- Bourke (2018). "Computer Science I", available at
https://cse.unl.edu/~cbourke/ComputerScienceOne.pdf
- Davies (2016). "The book of R". No starch press
- Cho, Martinez (2014). Statistics in MATLAB: A Primer. Chapman and Hall/CRC
Regolamento Esame
L'esame consiste in una prova pratica, in presenza, sulle tre parti del corso: R/Matlab, Stata e Python. Bisogna ottenere una valutazione positiva in tutte le parti per superare l'esame. L'esito dell'esame è una singola idoneità, che viene comunicata immediatamente. Risultati parzialmente positivi non esonerano dalle singole parti del corso in nessun caso.
Gli studenti che si ritirano, o che non risultino idonei, possono presentarsi ai successivi appelli d'esame della stessa sessione.
Durante le lezioni, si terranno alcune verifiche intermedie sull'apprendimento degli studenti. La valutazione positiva per una o più parti esonera lo studente per le stesse parti dall'esame finale. L'esonero rimarrà valido per l'intero anno accademico.
Gli studenti che non ottengono una valutazione positiva in tutte le tre parti dovranno sostenere l'esame finale su tutte le parti del corso nel quale non hanno ottenuto una valutazione positiva.
Gli studenti dovranno prenotare l'esame tramite Delphi ed essere presenti al giorno dell'esame. Lo stesso vale anche per gli studenti che ottengano un risultato positivo a tutte le prove intermedie.
Exam Rules
The exam consists of a practical test, in presence, on the three parts of the course: R/Matlab, Stata and Python. It is necessary to obtain a positive evaluation for all the three parts to pass the exam. The result of the exam is a single mark (pass or fail), and it is immediately communicated to the student. Partially positive results doesn't give any exemption on single parts of the course.
Students who withdraw or fail an exam may take the exam again in the same exam session.
During the period of the lessons, some intermediate tests will be held to verify the students' achievements. A positive evaluation in one or more parts will guarantee an exemption for those parts from the final exam. The exemption will remain valid for the entire academic year.
Students that don't obtain a positive evaluation in all the three parts will have to sit the final exam for all the parts in which they failed.
Students must book the exam through Delphi and be present on the exam date. The same also hold for students that obtain a positive evaluation to all the intermediate tests.
FRANCESCA MARAZZI
Programma
Nel secondo modulo, vengono introdotti Stata e Python (24 ore).
Stata: elementi di base (do files, dati e datasets), programmazione (macro, scalari, matrici e cicli), statistiche descrittive (grafici e tabelle), stima ed interpretazione del modello di regressione lineare.
Python: elementi di base, funzioni e oggetti, strutture dati, visualizzazione dei dati, applicazioni di python su modelli economici.
Program
During the second module, Stata and Python (24 hours) are introduced.
Stata: mechanics (do files, data and datasets), programming (macros, scalar, matrices, branching and looping), descriptives (graphs and tables), estimation and interpretation of the linear regression model.
Python: python essentials, functions and objects, data structures, data visualization, some applications of python on economic models.
Bibliografia
Letture suggerite
- Bourke (2018). "Computer Science I", disponibile al link
https://cse.unl.edu/~cbourke/ComputerScienceOne.pdf
- Microeconometrics using Stata, by A.C. Cameron and P.K. Trivedi, 2009
- Python for Everybody, Exploring Data using Python 3, by Charles Severance
- Stata documentation (any version)
- Christopher F. Baum (2016), An Introduction to Stata Programming, Second Edition, Stata Press
- An Introduction to Modern Econometrics using Stata, C.F. Baum, 2006
- Statistics with Stata, by L.C. Hamilton, 2006
- Mastering Metrics, by J. Angrist and S. Pischke, 2015
- An Introduction to Stata Programming, Christopher F. Baum, 2014
Bibliography
Suggested readings
- Bourke (2018). "Computer Science I", available at
https://cse.unl.edu/~cbourke/ComputerScienceOne.pdf
- Microeconometrics Using Stata, A. C. Cameron and P. K. Trivedi, Stata press
- Python for Everybody, Exploring Data using Python 3, by Charles Severance
- Stata documentation (any version)
- Christopher F. Baum (2016), An Introduction to Stata Programming, Second Edition, Stata Press
- An Introduction to Modern Econometrics using Stata, C.F. Baum, 2006
- Statistics with Stata, by L.C. Hamilton, 2006
- Mastering Metrics, by J. Angrist and S. Pischke, 2015
- An Introduction to Stata Programming, Christopher F. Baum, 2014
EN
IT
Updated A.Y. 2023-2024
Stata
The ultimate goal of the course is to enable students to answer a research question via processing and analyzing data on Stata, interpreting the output of the analysis, and presenting it in a publication-quality style. The course will cover the basics of Stata (syntax; do and dta files), data management, descriptive statistics (graphs and tables), estimation and interpretation of an OLS regression.
Assessment
During the course, there will be three hands-on-data sessions of 30 minutes, which will also constitute the intermediate evaluation (if attending in presence, see section Attendance). Passing the three exercises will guarantee an exemption for the Stata part of the Coding exam. The exemption will last for the current academic year.
Students not taking or passing the exercises can take the Stata part of the Coding exam on any regular call.
Attendance
The course is held in presence. Students with stringent motivations can attend online only in synchronous modality (recording is not allowed). Students following the course remotely can participate in the hands-on-data sessions to exercise, but this will not constitute an intermediate evaluation and, therefore, will not guarantee the exam exemption.
Software
Classes are held in a computer room. Students willing to use their laptops during classes are required to download and install Stata before the first class; to download the installer and activation codes, follow this link https://economia.uniroma2.it/en/def/software
References
Updated A.Y. 2023-2024
Stata
The ultimate goal of the course is to enable students to answer a research question via processing and analyzing data on Stata, interpreting the output of the analysis, and presenting it in a publication-quality style. The course will cover the basics of Stata (syntax; do and dta files), data management, descriptive statistics (graphs and tables), estimation and interpretation of an OLS regression.
Assessment
During the course, there will be three hands-on-data sessions of 30 minutes, which will also constitute the intermediate evaluation (if attending in presence, see section Attendance). Passing the three exercises will guarantee an exemption for the Stata part of the Coding exam. The exemption will last for the current academic year.
Students not taking or passing the exercises can take the Stata part of the Coding exam on any regular call.
Attendance
The course is held in presence. Students with stringent motivations can attend online only in synchronous modality (recording is not allowed). Students following the course remotely can participate in the hands-on-data sessions to exercise, but this will not constitute an intermediate evaluation and, therefore, will not guarantee the exam exemption.
Software
Classes are held in a computer room. Students willing to use their laptops during classes are required to download and install Stata before the first class; to download the installer and activation codes, follow this link https://economia.uniroma2.it/en/def/software
References
