ECONOMETRICS

This course is one of the core activities for students enrolled in the Finance curriculum of the Economics and Finance program. The course has the objective of providing the students with the competences that are required to analyse and measure economic and financial phenomena related in particular to financial markets, by means of up-to-date advanced statistical and econometric methods. The course aims to present the main econometric methods for univariate and multivariate regression models, with special reference to time series data and their application in finance.

Knowledge and competences:
- understand how to specify an econometric model starting from an economic model
- knowledge of the assumptions underlying each econometric model and command of the analytical tools needed for quantitative analyses
- understand the economic and financial phenomena related in particular to financial markets, by means of the most recent models of financial economics and econometrics

Application of acquired knowledge and skills:
- interpretation and management of financial dynamics, through the use of advanced analytical tools covered in the lectures
- being able to design useful strategies to measure and quantify economic phenomena and relationships among financial and macroeconomic variables
- know how to solve problems of particular interest in the econometrics for finance by exploiting analytical tools and empirical analyses

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

Mathematical Tools:
Matrix Algebra
Differential Calculus
Integral Calculus

Statistical Tools:
Random Variables and Distribution Theory
Point and Interval Estimation
Hypothesis Testing
Least Squares and Standard Linear Model

Part 1:
Univariate and multiple regression.
Hypothesis testing in regression models.

Part 2:
Estimation and inference for stationary time series models.
Forecasting.
Non-stationarity, spurious regression, and co-integration.

References:

- Stock, J. H., and M. W. Watson (2019), Introduction to Econometrics, 4th edition, Pearson.
- Ghysels, E., and M. Marcellino (2018), Applied economic forecasting using time series methods, Oxford University Press.
- Enders, W. (2015), Applied Econometric Time Series, 4th edition, Wiley.
- Neusser, K. (2016), Time Series Econometrics, Springer.

Written discussion of the estimation results and analytical solutions of advanced econometric problems.

Lectures, classes, empirical applications on economic and/or financial data using econometric software. Students will be encouraged to solve and to hand in some pieces of homework throughout the course.

English

Accessibility, Disability and Inclusion
Accommodation and support services for students with disabilities and students with specific learning impairments

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.

written and oral