First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Economics and Political Science
ECONOMICS AND FINANCE
Course unit
ADVANCED ECONOMETRICS
EPP3051595, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
ECONOMICS AND FINANCE
EP2422, Degree course structure A.Y. 2017/18, A.Y. 2018/19
N0
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Degree course track Common track
Number of ECTS credits allocated 10.0
Type of assessment Mark
Course unit English denomination ADVANCED ECONOMETRICS
Website of the academic structure http://www.economia.unipd.it
Department of reference Department of Economics and Management
E-Learning website https://elearning.unipd.it/economia/course/view.php?idnumber=2018-EP2422-000ZZ-2018-EPP3051595-N0
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit CANNOT be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge GUGLIELMO WEBER SECS-P/05
Other lecturers NUNZIO CAPPUCCIO SECS-P/05

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses SECS-P/05 Econometrics 10.0

Course unit organization
Period First semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 10.0 70 180.0 No turn

Calendar
Start of activities 01/10/2018
End of activities 18/01/2019

Syllabus
Prerequisites: As emphasis will be given to methods for empirical research in applied economics, students are required to have already developed good working knowledge of general concepts in statistics and econometrics.
Target skills and knowledge: Be able to critically appraise the usefulness and appropriateness of a variety of econometric procedures.

Understand how econometric methods can be used for the analysis of cross section and panel data.

Understand how econometric methods can be used for the analysis of time series data.
Examination methods: Written exam with questions on theory and exercises requiring the analysis of real data.
Assessment criteria: The exam wil be made of two separate parts: the first, on microeconometrics, will assign up to 18 marks (out of 30); the second, on macroeconometrics, will assign up to 12 marks.

Students will be able to take a mid-term exam at the end of the microeconometrics part (second half of November)
Course unit contents: This course introduces students to tools widely used in modern econometrics, discussing the properties of a variety of methods for the analysis of cross section, time series and panel data. Methods and applications will be soundly integrated.

Review of basic statistical notion. Classical linear regression model: specification, estimation and hypothesis testing. The linear regression model for cross section data. The generalised linear regression model for cross section data. The linear model with endogenous regressors: SIV, GIVE and GMM estimators. The Sargan and Hausman tests. The linear model for panel data(static and dynamic). Discrete choice models. Time series analysis: stazionarity and ergodicity. Univariate linear models for statioanry time series: the ARMA models. Treating non-stationary series: the ARIMA models. The dynamic linear model. The generalized linear model for time series data. Multivariate time series models.
Planned learning activities and teaching methods: Lectures. Classes.
Additional notes about suggested reading: Students with an inadequate econometric background can read selected chapters from Stock e Watson "Introduction to econometrics"
Textbooks (and optional supplementary readings)
  • Angrist, Joshua D.; Pischke, Jörn-Steffen, Mostly harmless econometricsan empiricist's compainonJoshua D. Angrist and Jörn-Steffen Pischke. Princeton: Princeton University press, 2009. Cerca nel catalogo
  • Wooldridge, Jeffrey M., Econometric analysis of cross section and panel data. Cambridge: The MIT Press, 2010. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Problem solving

Innovative teaching methods: Software or applications used
  • STATA

Sustainable Development Goals (SDGs)
No Poverty Quality Education Decent Work and Economic Growth