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ECONOMETRICS II
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Econometrics II
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Academic year 2020/2021
- Course ID
- SEM0094
- Teaching staff
- Alessandro Sembenelli (Lecturer)
Fabio Cesare Bagliano (Lecturer)
Marina Di Giacomo (Lecturer) - Year
- 1st year
- Teaching period
- Second semester
- Type
- Distinctive
- Credits/Recognition
- 12
- Course disciplinary sector (SSD)
- SECS-P/05 - econometria
- Delivery
- Formal authority
- Language
- English
- Attendance
- Optional
- Type of examination
- Written
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Sommario del corso
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Course objectives
The main purpose of this course is to give a general and comprehensive overview of the different econometric methodogies and approaches, focusing on what is relevant for doing and understanding empirical work. The number of econometric techniques that can be used is numerous and their validity often depends crucially upon the validity of the underlying assumption. This course attempts to guide students through this array of estimation and testing procedures by also offering several computer-lab sessions where students will face real world empirical cases.
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Results of learning outcomes
- Knowledge and understanding: this course will provide students with a deep and up-to-date knowledge of modern econometric theories and related estimation and testing techniques.
- Applying knowledge and understanding: students will learn how to apply econometrics techniques to actual economic problems. To this aim students will be introduced to a professional econometric software (E-views) which will be used for the computations presented in this course.
- Making judgements: the students will learn how to assess the validity of the assumptions of a wide range of econometric models with the purpose of realizing potential drawbacks or dangers in their application to relevant empirical economic questions.
- Communication skills: students will learn how to effectively organize ideas both in written and oral form, possibly with the help of presentation of scientific papers during the course.
- Learning skills: this course will enable students to understand the recent developments in econometrics and will be a suitable basis for further research work in the area.
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Course delivery
The course consists of 96 lecture hours. Strong interaction between teachers and students is warmly encouraged. Part of the course (24 hours) will be given at the Computer Lab.
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Learning assessment methods
2 h. and 30 m. (max.) written exam at the end of the course. The exam paper is structured in two parts ("Estimation" and "Time Series") and each part is made up of three questions. Specific quantitative scores are allocated to each question and communicated to students before the exam. Scores sum up to 30/30.
Specific exam rules for the Summer 2020 and Fall 2020 sessions (May-June-July and September exams) are detailed in the "Sample exam questions and exam rules" section on the Moodle page of the course. Note that also the September exam will be administered online using the Webex platform.
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Program
Note: For each topic, indication of relevant chapters of M. Verbeek "A Guide to Modern Econometrics" (4th edition, 2012) is provided.
Part 1
- The Classical Linear Regression Model and Its Violations (chap. 2-3-4)
- Endogeneity, Instrumental Variables and GMM (chap. 5)
- MaximumLikelihood Estimation and Specification Tests (chap. 6)
- Models with Limited Dependent Variables (chap. 7)Part 2
- Univariate Time Series Models (chap. 8)
- Multivariate Time Series Models (chap. 9)Suggested readings and bibliography
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The course is mostly based on M. Verbeek, "A Guide to Modern Econometrics" (4th edition, 2012). For most topics lecture notes will be also circulated.
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Class schedule
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Note
The methods of teaching activity could change in according to the limitation imposed by the current health crisis. In any case the e-learning mode is guaranteed throughout the academic year.Le modalità di svolgimento dell'attività didattica potranno subire variazioni in base alle limitazioni imposte dalla crisi sanitaria in corso. In ogni caso è assicurata la modalità a distanza per tutto l'anno accademico.- Oggetto:
