ECONOMETRICS

The course explores selected aspects of econometric methods, with a focus on univariate and multivariate regression models using both cross-sectional and time series data. In particular, it introduces the concepts of exogeneity, stationarity, and cointegration, which are key notions in economics and econometrics. The aim is to equip students with the essential econometric tools needed for the measurement, interpretation, and forecasting of economic and financial phenomena. The course places strong emphasis on applied econometric practice.

Knowledge and Understanding

Attendance and active participation in lectures, tutorials, and tutoring activities, together with independent study, will enable students to acquire the following knowledge and understanding:
- knowledge of the theoretical foundations of econometric models and methods
- understanding of model specification, inference, and forecasting in regression models
- ability to analyze, understand, and interpret economic and financial phenomena using econometric tools

Applying Knowledge and Understanding

Through interaction with instructors, teaching assistants, tutors, and peers, as well as through independent study, students will develop the ability to effectively apply their theoretical knowledge in order to:
- use both classical and modern analytical tools, as well as formal analytical derivations, to understand relevant economic relationships
- work with macroeconomic and financial data for model specification, estimation, and forecasting using regression models
-analyze current issues in the real economy and financial markets

Making Judgements, Communication Skills, and Learning Skills

Through both individual and group work on the topics covered in class, students will be able to:
- interpret and manage economic dynamics using advanced analytical tools
- interpret the results produced by econometric software
- critically assess the strengths and limitations of analytical methods and their applications
- read and evaluate the results of empirical analyses in a critical manner

Elements of statistical inference: Estimation, hypothesis testing (critical values and p-values) and confidence intervals (at least for the sampling average).

1. Introduction to the linear regression model
1.1. Introduction
1.2. Bivariate regression model
1.3. Multivariate regression model
1.4 Examples

2. Asymptotic properties of the estimators
2.1. Asymptotic properties of the OLS estimator
2.2. Inference
2.3 Examples

3. Univariate stationary stochastic processes
3.1 Regression models with time series data
3.2 AR, MA and ARMA models
3.3 Examples

4 Univariate non-stationary stochastic processes
4.1. Processes with unity roots and spurious regression
4.2. Trend Stationary processes (TS) and Differences Stationary (DS)
4.3. Introduction to cointegration
4.4. Examples

References:

- R. C. Hill, W. E. Griffiths G. C. Lim (2013), Principles of Econometrics, (5th edition), Wiley ed.


Optional (advanced):
- Hamilton J.(1995), Time Series Econometrics
- Wooldridge J. Introductory Econometrics: A Modern Approach 7th, ed. Cengage Learning

The exam is written and last for about 90 minutes. The questions will consider both theoretical and empirical topics considered in class. Questions mostly regard the ability to interpret and making inference from standard output obtained from standard econometric software.

written and oral

The lecturer has a duty to ensure that the rules regarding the authenticity and originality of exam tests and papers are respected. Therefore, if there is suspicion of irregular conduct, an additional assessment may be conducted, which could differ from the original exam description.

The exam consists of 10 questions, each worth between 0 and 4 points. The final grade is determined by evaluating the overall quality of the exam.

Lectures, tutorial exercises, practical application on economic and/or financial data using an econometric software

Ca’ Foscari abides by Italian Law (Law 17/1999; Law 170/2010) regarding support services and accommodation available to students with disabilities. This includes students with mobility, visual, hearing and other disabilities (Law 17/1999), and specific learning impairments (Law 170/2010). If you have a disability or impairment that requires accommodations (i.e., alternate testing, readers, note takers or interpreters) please contact the Disability and Accessibility Offices in Student Services: disabilita@unive.it.