MICROECONOMETRICS
Syllabus
Obiettivi Formativi
Conoscenza e capacità di comprensione: Al completamento con successo del corso, gli studenti saranno in grado di comprendere i principali strumenti per condurre inferenza causale. Nello specifico, studieremo il modello degli outcome potenziali, discuteremo le principali assunzioni necessarie per l'identificazione degli effetti causali medi, e ci concentreremo sui principali metodi di stima. L'accento sarà posto sull'identificazione e sulla stima consistente.
Capacità di applicare conoscenza e comprensione: Al termine del corso, gli studenti saranno in grado di applicare le tecniche discusse utilizzando il software statistico Stata.
Autonomia di giudizio: Al termine del corso, gli studenti saranno in grado di orientarsi tra diverse tecniche e scegliere la migliore per il caso in questione.
Abilità comunicative: Lo studente dovrà saper presentare, anche con l'ausilio degli opportuni strumenti informatici, i risultati delle proprie elaborazioni e analisi sia ad un pubblico di interlocutori esperti che non esperti.
Learning Objectives
Knowledge and understanding: On successful completion of the course, students will be able to understand causal inference tools. Specifically, we will study the potential outcome model, discuss the main assumptions required for the identification of average causal effects, and focus on the main estimation approaches. Emphasis will be placed on identification and consistent estimation.
Applying knowledge and understanding: On successful completion of the course, students will be able to apply the discussed techniques using the Stata statistical software.
Making judgements: On successful completion of the course, students will be able to orient themselves between different techniques and choose the best for the case at hand.
Communication skills: Students will be able to present and effectively communicate the results of their own elaborations and analyses.
FEDERICO BELOTTI
Prerequisiti
Prerequisites
Programma
- Modelli per dati longitudinali
Modelli statici
Stima ad effetti fissi e casuali
Test di specificazione
Estensioni
Difference-in-differences
Modelli dinamici
- Modelli per variabili dipendenti limitate
Introduzione ai metodi bootstrap, jackknife e delta per la stima della varianza
Variabili dipendenti binarie
Variabili dipendenti discrete
Variabili dipendenti di conteggio
Troncamento e selezione del campione
Program
- Linear panel data models
Static models and main assumptions
Fixed- and random-effects estimation
Homogeneity and specification tests
Extensions
Difference-in-differences
Dynamic models
- Models for limited dependent variables
A short introduction to bootstrap, jackknife and delta methods for variance estimation
Binary outcomes
Multinomial outcomes
Count outcomes
Truncation and sample selection
Testi Adottati
Per alcuni argomenti, Wooldridge (2010) sarà integrato da articoli selezionati che saranno resi disponibili nella sezione materiali del sito web del corso e da alcuni capitoli tratti da:
Greene W.H., Econometric Analysis, 8th ed., Pearson.
Books
For some topics, Wooldridge (2010) will be complemented by selected articles which will be made available on the material section of the course website and by some chapters from:
Greene W.H., Econometric Analysis, 8th ed., Pearson.
Bibliografia
Peracchi F. (2001), Econometrics, Wiley, Chichester (UK).
Davidson R. and MacKinnon J.G., Econometric Theory and Methods, New York, Oxford University Press, 2004.
Slides, appunti e ulteriori riferimenti verranno forniti in classe durante il corso.
Bibliography
Peracchi F. (2001), Econometrics, Wiley, Chichester (UK).
Davidson R. and MacKinnon J.G., Econometric Theory and Methods, New York, Oxford University Press, 2004.
Teaching notes and suggestions for further reading will be provided in class.
Modalità di svolgimento
Teaching methods
Regolamento Esame
Gli studenti devono prenotarsi all'esame finale su https://delphi.uniroma2.it. Gli studenti che falliscono o rinunciano all'esame possono sostenerlo nuovamente nella stessa sessione di esami.
Exam Rules
Students must book for the final exam on https://delphi.uniroma2.it. Students who fail or withdraw from the exam may take it again in the same exam session.
FEDERICO BELOTTI
Obiettivi Formativi
Conoscenza e capacità di comprensione: Al completamento con successo del corso, gli studenti saranno in grado di comprendere i principali strumenti per condurre inferenza causale. Nello specifico, studieremo il modello degli outcome potenziali, discuteremo le principali assunzioni necessarie per l'identificazione degli effetti causali medi, e ci concentreremo sui principali metodi di stima. L'accento sarà posto sull'identificazione e sulla stima consistente.
Capacità di applicare conoscenza e comprensione: Al termine del corso, gli studenti saranno in grado di applicare le tecniche discusse utilizzando il software statistico Stata.
Autonomia di giudizio: Al termine del corso, gli studenti saranno in grado di orientarsi tra diverse tecniche e scegliere la migliore per il caso in questione.
Abilità comunicative: Lo studente dovrà saper presentare, anche con l'ausilio degli opportuni strumenti informatici, i risultati delle proprie elaborazioni e analisi sia ad un pubblico di interlocutori esperti che non esperti.
Learning Objectives
Knowledge and understanding: On successful completion of the course, students will be able to understand causal inference tools. Specifically, we will study the potential outcome model, discuss the main assumptions required for the identification of average causal effects, and focus on the main estimation approaches. Emphasis will be placed on identification and consistent estimation.
Applying knowledge and understanding: On successful completion of the course, students will be able to apply the discussed techniques using the Stata statistical software.
Making judgements: On successful completion of the course, students will be able to orient themselves between different techniques and choose the best for the case at hand.
Communication skills: Students will be able to present and effectively communicate the results of their own elaborations and analyses.
Prerequisiti
Prerequisites
Programma
Regression adjustment
Propensity score
Matching
Regression discontinuity
Dati longitudinali
Effetti fissi e casuali
Test di specificazione
Difference-in-differences
Controlli sintetici
Effetti fissi interattivi e controlli sintetici generalizzati
Program
Regression adjustment
Propensity score estimators
Matching estimators
Regression discontinuity design
Panel data
Fixed- and random-effects
Tests of specification
Difference-in-differences
Synthetic controls
Interactive fixed-effects and generalized synthetic controls
Testi Adottati
Per alcuni argomenti, Cunningham (2021) sarà integrato da articoli selezionati che saranno resi disponibili nella sezione materiali del sito web del corso e da alcuni capitoli tratti da:
Angrist J.D., and Pischke J.-S., (2009), Mostly Harmless Econometrics: An Empiricists’ Companion, Princeton University Press, Princeton.
Wooldridge J.M., (2010), Econometric Analysis of Cross-Section and Panel Data, 2nd ed., MIT Press, Cambridge (MA).
Books
For some topics, Cunningham (2021) will be complemented by selected articles which will be made available on the material section of the course website and by some chapters from:
Wooldridge J.M., (2010), Econometric Analysis of Cross-Section and Panel Data, 2nd ed., MIT Press, Cambridge (MA);
Angrist J.D., and Pischke J.-S., (2009), Mostly Harmless Econometrics: An Empiricists’ Companion, Princeton University Press, Princeton.
Bibliografia
Cunningham S., (2021) Causal Inference: The Mixtape. Yale University Press;
Angrist J.D., and Pischke J.-S., (2009), Mostly Harmless Econometrics: An Empiricists’ Companion, Princeton University Press, Princeton.
Teaching notes and suggestions for further reading will be provided in class.
Bibliography
Cunningham S., (2021) Causal Inference: The Mixtape. Yale University Press;
Angrist J.D., and Pischke J.-S., (2009), Mostly Harmless Econometrics: An Empiricists’ Companion, Princeton University Press, Princeton.
Teaching notes and suggestions for further reading will be provided in class.
Modalità di svolgimento
Teaching methods
Regolamento Esame
Inoltre, gli studenti frequentanti, a gruppi di due, dovranno presentare in classe un articolo accademico pubblicato che sfrutti uno dei metodi trattati nel corso. Il voto della presentazione andrà da 1 a 4 e sarà sommato al voto dell'esame finale (solo nell'appello d'esame subito dopo il corso). In caso di annullamento della presentazione programmata, il voto dell’esame finale sarà ridotto di 2 punti.
Gli studenti devono prenotarsi all'esame finale su https://delphi.uniroma2.it. Gli studenti che falliscono o rinunciano all'esame possono sostenerlo nuovamente nello stesso appello.
Exam Rules
Additionally, attending students, in groups of two, have to give a class presentation of a published academic paper that exploits one of the methods covered in the course. The mark of the presentation will range from 1 to 4 and will be added to the mark of the final exam (only in the exam session just after the course). Withdrawals from scheduled presentations will be marked with -2.
Students must book for the final exam on https://delphi.uniroma2.it. Students who fail or withdraw from the exam may take it again in the same session. Class presentations will take place in last 2 classes (30 min presentations, inclusing questions and discussion)
Obiettivi Formativi
CONOSCENZA E CAPACITÀ DI COMPRENSIONE: Sulla base di una comprensione sistematica e consapevole delle tecniche econometriche trattate, lo studente dovrà saper elaborare idee originali per rispondere a domande di interesse nell'ambito della letteratura economica.
CAPACITÀ DI APPLICARE CONOSCENZA E COMPRENSIONE: Sulla base degli strumenti analitici e delle conoscenze acquisiti attraverso lezioni teoriche ed esercitazioni guidate, lo studente dovrà saper applicare in modo appropriato le tecniche econometriche trattate usando software statistici come Stata e/o R. In particolare, dovrà saper specificare correttamente un modello di regressione, scegliere il metodo di stima più appropriato e interpretare correttamente i risultati.
AUTONOMIA DI GIUDIZIO: Lo studente dovrà avere la capacità di integrare autonomamente le conoscenze acquisite per gestire analisi empiriche anche complesse, definendone gli obiettivi, formulando ipotesi, ricercando autonomamente le informazioni ed i dati necessari per lo svolgimento dell'analisi, motivando la scelta della metodologia più appropriata e interpretando i risultati ottenuti al fine di ricavarne indicazioni e implicazioni utili per il problema analizzato.
ABILITÀ COMUNICATIVE: Lo studente dovrà saper presentare, anche con l'ausilio degli opportuni strumenti informatici, i risultati delle proprie elaborazioni e analisi sia ad un pubblico di interlocutori esperti che non esperti.
CAPACITÀ DI APPRENDIMENTO: Lo studente dovrà saper studiare autonomamente, sviluppando le capacità di apprendimento necessarie per affrontare corsi di econometria piu' avanzati o per intraprendere le analisi quantitative richieste in altri corsi o per la tesi di laurea.
Learning Objectives
KNOWLEDGE AND UNDERSTANDING: Based on a systematic and conscious understanding of the discussed techniques, students should be able to elaborate original ideas to answer economic questions of interest.
APPLYING KNOWLEDGE AND UNDERSTANDING: Based on the analytical tools and the knowledge acquired through theoretical and practice sessions, students should be able to apply appropriately the discussed techniques using statistical software such as Stata and/or R. In particular, they will need to be able to correctly specify a regression model, choose the most appropriate estimation approach and correctly interpret the empirical results.
MAKING JUDGEMENTS: Students should be able to autonomously integrate the acquired knowledge in order to manage complex empirical analyses, by appropriately defining the objectives, by formulating hypotheses, by autonomously searching for the information and data necessary for carrying out the analysis, by motivating the choice of the most appropriate methodology and by extracting useful strategic indications and/or policy implications based on the empirical results.
COMMUNICATION SKILLS: Students should to be able to present the results of their own elaborations and analyses both to an expert and non-experts audience.
LEARNING SKILLS: Student should be able to study independently, developing the learning skills needed to tackle more advanced econometrics courses or to undertake the quantitative analyses required in other courses or for the final dissertation.
Prerequisiti
Prerequisites
Programma
Modelli per variabili dipendenti binarie, politomiche e ordinabili
Modelli per variabili dipendenti basate su frequenze/conteggi
Inferenza causale
Il framework degli outcomes potenziali
Effetto di trattamento/intervento e selection bias
Principali assunzioni
Regression adjustment, propensity score e metodi basati sul matching
Regression discontinuity
Dati longitudinali
Difference-in-differences
Controlli sintetici
Program
Binary and multinomial outcomes
Count data outcomes
Causal inference
Potential outcomes perspective
Treatment effects and selection bias
Main assumptions and estimands
Regression, matching and propensity score estimators
Regression discontinuity design
Panel data
Difference-in-differences
(Generalized) Synthetic controls
Testi Adottati
Per alcuni argomenti, Wooldridge (2010) sarà integrato da articoli selezionati che saranno resi disponibili nella sezione materiali del sito web del corso e da alcuni capitoli tratti da:
Cunningham S., (2021) Causal Inference: The Mixtape. Yale University Press;
Angrist J.D., and Pischke J.-S., (2009), Mostly Harmless Econometrics: An Empiricists’ Companion, Princeton University Press, Princeton.
Books
For some topics, Wooldridge (2010) will be complemented by selected articles which will be made available on the material section of the course website and by some chapters from:
Cunningham S., (2021) Causal Inference: The Mixtape. Yale University Press;
Angrist J.D., and Pischke J.-S., (2009), Mostly Harmless Econometrics: An Empiricists’ Companion, Princeton University Press, Princeton.
Bibliografia
Cunningham S., (2021) Causal Inference: The Mixtape. Yale University Press;
Angrist J.D., and Pischke J.-S., (2009), Mostly Harmless Econometrics: An Empiricists’ Companion, Princeton University Press, Princeton.
Teaching notes and suggestions for further reading will be provided in class.
Bibliography
Cunningham S., (2021) Causal Inference: The Mixtape. Yale University Press;
Angrist J.D., and Pischke J.-S., (2009), Mostly Harmless Econometrics: An Empiricists’ Companion, Princeton University Press, Princeton.
Teaching notes and suggestions for further reading will be provided in class.
Modalità di svolgimento
Teaching methods
Regolamento Esame
Progetto empirico: il tema e la domanda di ricerca dovranno essere scelti da ciascuno studente, sono ammessi gruppi con un massimo di 3 studenti. Ciascuno studente/gruppo sarà responsabile di comunicare al docente il tema di interesse, le fonti di dati che si intende utilizzare, e la tecnica econometrica (tra quelle incluse nel programma del corso) che si prevede di applicare. Solo dopo avere ottenuto un esplicito assenso si potrà procedere con il progetto. Ciascuno studente/gruppo sarà responsabile di una presentazione finale del lavoro (circa 30 minuti). Tutti gli studenti appartenenti ad un determinato gruppo dovranno essere in grado di presentare l'analisi effettuata. Gli ultimi 5 minuti saranno riservati a commenti/domande. Ciascuno/studente gruppo dovra' rendere disponibile la presentazione al resto della classe. Per ciascuno studente verra' valutata la chiarezza espositiva e la capacità di rispondere ad eventuali quesiti/commenti.
Exam Rules
Empirical project: the topic and the research question must be chosen by each student, groups with a maximum of 3 students are allowed. Each student / group will be responsible for communicating to the teacher the topic of interest, the data sources to be used, and the econometric technique (among those included in the course program) to be applied. All projects must be approved in advance. Each student / group will be responsible for a final presentation of the work (about 30 minutes). All students belonging to a certain group must be able to present the analysis carried out. The last 5 minutes will be reserved for comments / questions. Each group / student must make the presentation available to the rest of the class. For each student, the clarity of presentation and the ability to answer any questions / comments will be assessed.
Updated A.Y. 2021-2022
Updated A.Y. 2021-2022
OVERVIEW AND PREREQUISITIES
The aim of this course is to provide a survey of state-of-the-art microeconometric techniques. We will cover linear models for longitudinal data and a variety of causal inference designs and methods. We will discuss the strengths and weaknesses of these tools using economic applications.
A willingness to work hard on possibly unfamiliar material is key and, in addition to the material covered in the Mathematics (8011190), Statistics (8010848) and Econometrics (8011571) courses, I also expect that you are reasonably proficient with the Stata statistical software.
OUTLINE
- Causal inference: main assumptions and estimands
- Regression (reloaded)
- Propensity score estimators
- Matching
- Matching on the PS
- Regression discontinuity
- Sharp design
- Fuzzy design
- Panel data
- Pooling
- Random-effects
- Fixed-effects (within group and first-differences estimators)
- Interactive fixed-effects
- Correlated random-effects
- Hausman type tests
- Difference-in-differences
- (Generalized) Synthetic controls
TEXTBOOKS AND MATERIAL
The main references are Wooldridge (2010) and Cunningham (2021). For further study in microeconometrics I suggest: Hansen (2021), Hsiao (2014), Cameron and Trivedi (2005) and Peracchi (2001). Angrist and Pischke (2009) for applied issues. Lecture notes, slides and Stata codes will be posted in the material section. Suggestions for further reading will be provided in class.
Wooldridge J.M., (2010), Econometric Analysis of Cross-Section and Panel Data, 2nd ed., MIT Press, Cambridge (MA).
Hansen B.E., (2020), Econometrics, download the book here.
Peracchi F. (2001), Econometrics, Wiley, Chichester (UK).
Cameron C., Trivedi P.K., (2005), Microeconometrics: methods and applications, Cambridge University Press.
Hsiao C.,(2014), Analysis of Panel Data. Cambridge University Press, New York, NY, 3rd edition.
For a focus on applied issues see:
Cunningham S., (2021) Causal Inference: The Mixtape. Yale University Press.
Angrist J.D., and Pischke J.-S., (2009), Mostly Harmless Econometrics: An Empiricists’s Companion, Princeton University Press, Princeton.
Updated A.Y. 2020-2021
Updated A.Y. 2020-2021
OVERVIEW AND PREREQUISITIES
The aim of this course is to provide a survey of state-of-the-art microeconometric techniques. We will cover limited dependent variable models, linear models for longitudinal data and a variety of causal inference designs and methods. We will discuss the strengths and weaknesses of these tools using economic applications.
A willingness to work hard on possibly unfamiliar material is key and, in addition to the material covered in the Mathematics (8011190), Statistics (8010848) and Econometrics (8011571) courses, I also expect that you are reasonably proficient in the statistical software Stata or R.
OUTLINE
Linear panel data models:
- Pooling
- Random-effects
- Fixed-effects (within group and first-differences estimators)
- Interactive fixed-effects
- Correlated random-effects
- Hausman type tests
Cross-sectional and panel data models for discrete and limited dependent variables:
- Binary outcomes models
- Multinomial outcomes models
- Count data models
- Models for truncated/censored data and sample selection models (optional)
Estimating Average Treatment Effects:
- Treatment effects and selection bias
- Matching and propensity score estimators
- IV reloaded: the Local Average Treatment Effect estimator
- Regression discontinuity
- Differences-in-differences estimators
- Generalized synthetic control
TEXTBOOK AND MATERIAL
Wooldridge J.M., (2010), Econometric Analysis of Cross-Section and Panel Data, 2nd ed., MIT Press, Cambridge (MA).
Lecture slides and a reading list will be posted on the course web site.
USEFUL REFERENCES
Hsiao C., Analysis of Panel Data. Cambridge University Press, New York, NY, 3rd edition, 2014.
Angrist J.D., and Pischke J.-S., (2009), Mostly Harmless Econometrics: An Empiricists’s Companion, Princeton University Press, Princeton.
Peracchi F., (2001), Econometrics, Wiley, Chichester (UK).
Train, K. E., (2009), Discrete Choice Methods with Simulation, 2nd ed. New York: Cambridge University Press.
KNOWLEDGE AND UNDERSTANDING
Based on a systematic and conscious understanding of the discussed techniques, students should be able to elaborate original ideas to answer economic questions of interest.
APPLYING KNOWLEDGE AND UNDERSTANDING
Based on the analytical tools and the knowledge acquired through theoretical and practical sessions, students should be able to apply appropriately the discussed techniques using statistical software such as Stata and/or R.
MAKING JUDGEMENTS
Students should be able to autonomously integrate the acquired knowledge in order to manage complex empirical analyses, by appropriately defining the objectives, by formulating hypotheses, by autonomously searching for the information and data necessary for carrying out the analysis, by motivating the choice of the most appropriate methodology and by extracting useful strategic indications and/or policy implications based on the empirical results.
COMMUNICATION SKILLS
Students should to be able to present the results of their own elaborations and analyses both to an expert and non-experts audience.
LEARNING SKILLS
Student should be able to study independently, developing the learning skills needed to tackle more advanced econometrics courses or to undertake the quantitative analyses required in other courses or for the final dissertation.
Updated A.Y. 2019-2020
Updated A.Y. 2019-2020
OVERVIEW AND PREREQUISITIES
The aim of this course is to deepen the topics covered in the econometrics course by introducing the student to the state-of-the-art techniques for estimating causal effects, the dynamic linear panel data model and to the limited dependent variable models for both cross-sectional and longitudinal data. The course consists of eighteen theoretical lectures, with applications based on economic micro-data. Students should have completed Mathematics (8011190), Statistics (8010848) and Econometrics (8011571). A good knowledge of Stata and/or R is required.
OUTLINE
Dynamic linear panel data models
Cross-sectional and panel data models for discrete and limited dependent variables:
- Binary outcomes models
- Multinomial outcomes models
- Count data models
- Models for truncated/censored data and sample selection models
Estimating Average Treatment Effects:
- Setup and main assumptions: treatment effects and selection bias
- Matching and propensity score estimators
- Differences-in-differences estimators
- Instrumental variables estimators
- Regression discontinuity
TEXTBOOK AND MATERIAL
Wooldridge J.M., (2010), Econometric Analysis of Cross-Section and Panel Data, 2nd ed., MIT Press, Cambridge (MA).
Lecture slides will be posted on the course web site.
READING LIST
Arellano M., Bond S., (1991), Some tests of specification for panel data: Monte carlo evidence and an application to employment equations, Review of Economic Studies, 58:277-297.
Blundell R., Bond S., (1998), Initial conditions and moment restrictions in dynamic panel data models, Journal of Econometrics, 87:115-143.
Bond S., (2002), Dynamic panel data models: A guide to micro data methods and practice, Portuguese Economic Journal, 1(2):141-162
Gourieroux C., Monfort A., Renault E., Trognon A., (1987), Generalised residuals, Journal of Econometrics, 34:5-32
Imbens G.W., Lemieux T., (2008), Regression discontinuity designs: A guide to practice, Journal of Econometrics, 142(2):615-635.
Lewbel A., Dong Y., Yang T.T., (2012), Comparing features of convenient estimators for binary choice models with endogenous regressors, Canadian Journal of Economics, 45(3): 809-829.
Roodman D.M., (2009), A note on the theme of too many instruments, Oxford Bulletin of Economics and Statistics, 71:135-158.
Roodman, D.M, (2009), How to do xtabond2: An Introduction to Difference and System GMM in Stata. The Stata Journal, 9(1):86-136.
Wilde, J. (2000), Identification of multiple equation probit models with endogenous dummy regressors, Economics Letters, 69:309-312
Wooldridge, J.M. (2005), Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity, Journal of Applied Econometrics, 20: 39-54.
Suggestions for further reading will be provided in class.
USEFUL REFERENCES
Hsiao C., Analysis of Panel Data. Cambridge University Press, New York, NY, 3rd edition, 2014.
Angrist J.D., and Pischke J.-S., (2009), Mostly Harmless Econometrics: An Empiricists’s Companion, Princeton University Press, Princeton.
Peracchi F., (2001), Econometrics, Wiley, Chichester (UK).
Train, K. E., (2009), Discrete Choice Methods with Simulation, 2nd ed. New York: Cambridge University Press. Downloadable here
KNOWLEDGE AND UNDERSTANDING
Based on a systematic and conscious understanding of the discussed techniques, students should be able to elaborate original ideas to answer economic questions of interest.
APPLYING KNOWLEDGE AND UNDERSTANDING
Based on the analytical tools and the knowledge acquired through theoretical and practice sessions, students should be able to apply appropriately the discussed techniques using statistical software such as Stata and/or R. In particular, they will need to be able to correctly specify a regression model, choose the most appropriate estimation approach and correctly interpret the empirical results.
MAKING JUDGEMENTS
Students should be able to autonomously integrate the acquired knowledge in order to manage complex empirical analyses, by appropriately defining the objectives, by formulating hypotheses, by autonomously searching for the information and data necessary for carrying out the analysis, by motivating the choice of the most appropriate methodology and by extracting useful strategic indications and/or policy implications based on the empirical results.
COMMUNICATION SKILLS
Students should to be able to present the results of their own elaborations and analyses both to an expert and non-experts audience.
LEARNING SKILLS
Student should be able to study independently, developing the learning skills needed to tackle more advanced econometrics courses or to undertake the quantitative analyses required in other courses or for the final dissertation.
Updated A.Y. 2018-2019
Updated A.Y. 2018-2019
Overview and requirements
The course provides a detailed overview of microeconometric methods for both cross-sectional and panel data. It focuses on nonlinear and advanced panel data models, with special emphasis on intuitions and applications. The relevant methodologies are discussed in class and then implemented during lab sessions, where students will have the opportunity to gain hands-on experience using the Stata statistical software. At the end of the course students should be able to specify the appropriate econometric model for investigating the problem under study and draw proper conclusions based on the results. This course is taught at a level assuming comfort with the course content in Mathematics (8011190), Statistics (8010848), and Econometrics (8011571).
Outline
- Cross-sectional Data Models for Discrete and Limited Dependent Variables
- Binary outcomes models
- Multinomial outcomes models
- Models for truncated/censored data and sample selection models
- Count data models
- Panel Data Models
- Static linear panel data models: within group, first-difference, GLS and between group estimators
- Dynamic linear panel data models
- Nonlinear models: binary, truncated/censored and count outcomes
- (Optional: Spatial panel data models)
- Treatment Evaluation
- Setup and main assumptions
- Treatment effects and selection bias
- Matching and propensity score estimators
- Differences-in-differences estimators
- Regression discontinuity design
References and material
- Cameron A.C. and Trivedi P.K. (2005), Microeconometrics, Cambridge University Press, New York.
- Peracchi F. (2001), Econometrics, Wiley, Chichester (UK).
- Wooldridge J.M. (2010), Econometric Analysis of Cross-Section and Panel Data, 2nd ed., MIT Press, Cambridge (MA).
The material, including lecture slides and related Stata tutorials, will be posted on the course web site. The main textbook is Wooldridge (2010), but it may be complemented by some chapters from Cameron and Trivedi (2005) and Peracchi (2001).
Updated A.Y. 2017-2018
Updated A.Y. 2017-2018
Overview and requirements
The course provides an overview of microeconometric methods for both cross-sectional and panel data and is structured so as to have a practical flavor, with a special emphasis on intuitions and applications. It combines standard lectures on microeconometric techniques together with tutorial lectures illustrating how the theory can be put to effective use through the Stata statistical software. It is especially suitable for students who want to address an empirical question in their master's thesis. After following the course, students should be able to understand when it is appropriate to apply a given method, the assumptions under which it is valid, and how it can be practically implemented.
The course is taught at a level that assumes comfort with the course content in Mathematics (8011190), Statistics (8010848), and Econometrics (8011571).
Outline
- Introduction and Background
- Conditional expectations
- Partial effects
- Cross-sectional Data Models for Discrete and Limited Dependent Variables
- Binary outcomes models
- Multinomial outcomes models
- Models for truncated/censored data and sample selection models
- Count data models
- Panel Data Models
- Static linear panel data models: within group, first-difference, GLS and between group estimators
- Repeated cross-sections, heterogeneous panels, unbalanced panels and panel attrition
- Dynamic linear panel data models
- Nonlinear models: binary, truncated/censored and count outcomes
- Treatment Evaluation
- Setup and main assumptions
- Treatment effects and selection bias
- Matching and propensity score estimators
- Differences-in-differences estimators
- Regression discontinuity design
References and material
- Cameron A.C. and Trivedi P.K. (2005), Microeconometrics, Cambridge University Press, New York.
- Peracchi F. (2001), Econometrics, Wiley, Chichester (UK).
- Wooldridge J.M. (2010), Econometric Analysis of Cross-Section and Panel Data, 2nd ed., MIT Press, Cambridge (MA).
The material, including lecture notes and related Stata tutorials, will be posted on the course web site. The main textbook is Wooldridge (2010), but it may be complemented by some chapters from Cameron and Trivedi (2005) and Peracchi (2001).
Updated A.Y. 2016-2017
Updated A.Y. 2016-2017
Overview and requirements
The course provides an overview of microeconometric methods for both cross-sectional and panel data and is structured so as to have a practical flavor, with a special emphasis on intuitions and applications. It combines standard lectures on microeconometric techniques together with tutorial lectures illustrating how the theory can be put to effective use through the Stata statistical software. It is especially suitable for students who want to address an empirical question in their master's thesis. After following the course, students should be able to understand when it is appropriate to apply a given method, the assumptions under which it is valid, and how it can be practically implemented.
The course is taught at a level that assumes comfort with the course content in Mathematics (8011190), Statistics (8010848), and Econometrics (8011571).
Outline
- Introduction and Background
- Conditional expectations
- Partial effects
- Cross-sectional Data Models for Discrete and Limited Dependent Variables
- Binary outcomes models
- Multinomial outcomes models
- Models for truncated/censored data and sample selection models
- Count data models
- Panel Data Models
- Static linear panel data models: within group, first-difference, GLS and between group estimators
- Repeated cross-sections, heterogeneous panels, unbalanced panels and panel attrition
- Dynamic linear panel data models
- Nonlinear models: binary, truncated/censored and count outcomes
- Treatment Evaluation
- Setup and main assumptions
- Treatment effects and selection bias
- Matching and propensity score estimators
- Differences-in-differences estimators
- Regression discontinuity design
References and material
- Cameron A.C. and Trivedi P.K. (2005), Microeconometrics, Cambridge University Press, New York.
- Peracchi F. (2001), Econometrics, Wiley, Chichester (UK).
- Wooldridge J.M. (2010), Econometric Analysis of Cross-Section and Panel Data, 2nd ed., MIT Press, Cambridge (MA).
The material, including lecture notes and related Stata tutorials, will be posted on the course web site. The main textbook is Wooldridge (2010), but it may be complemented by some chapters from Cameron and Trivedi (2005) and Peracchi (2001).