Econometrics

Academic year
2021/2022 Syllabus of previous years
Official course title
Econometrics
Course code
PHD143 (AF:364613 AR:193154)
Modality
On campus classes
ECTS credits
3 out of 6 of Econometrics and Machine Learning
Degree level
Corso di Dottorato (D.M.45)
Educational sector code
SECS-P/05
Period
1st Semester
Course year
1
Where
VENEZIA
Moodle
Go to Moodle page
The course will provide students with the essential econometric tools needed for climate change analysis. The course contributes to achieve the main objectives of the PhD Programme in Science and Management of Climate Change. It will teach students to design useful strategies to measure and quantify economic phenomena such as climate change and to specify proper econometric models from economic theory.
Knowledge and competences:
- understand how to specify an econometric model starting from an economic model
- understand the implications of the assumptions underlying each econometric model and recognize potential violations of those assumptions

Application of acquired knowledge and skills:
- being able to design useful strategies to measure and quantify economic phenomena such as climate change
- exploit the statistical and econometric tools studied throughout the course to conduct empirical research at the PhD level

Judgement and interpretation skills:
- evaluate strengths and weaknesses of the methodologies analysed and of their empirical application
- being able to critically interpret the outcomes of empirical analyses and of scientific papers
Undergraduate-level notions of calculus, statistics, and basic notions of microeconomics, macroeconomics.
REGRESSION ANALYSIS WITH CROSS-SECTIONAL DATA
- The Simple and Multiple Regression Models: OLS Estimation, Inference and Asymptotics
- Heteroskedasticity and Autocorrelation: the GLS estimator
- Endogeneity: the Instrumental Variable estimator
- Quadratic forms, interaction terms, adjusted R-squared
- Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables
- Heteroskedasticity
- Model misspecification

REGRESSION ANALYSIS WITH LIMITED DEPENDENT VARIABLES
- Linear probability model, Logit and Probit Models

REGRESSION ANALYSIS WITH PANEL DATA
- Pooling Cross Sections Across Time: Simple Panel Data Methods and Fixed Effects Estimation
- Random Effect Estimation
Wooldridge, J.M., Introductory Econometrics: A Modern Approach, Fifth Edition
South-Western College Publishing, 2013
Final take home exam or empirical project
lectures and practical sessions
written

This subject deals with topics related to the macro-area "Climate change and energy" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development

Definitive programme.