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: REGRESSION ANALYSIS

1. The Multiple Linear Regression Model
- Matrix formulation of the k-Variable Model; The algebra of least squares; Partial correlation coefficients; Geometry of least squares; Inference in the k-variable equation; Prediction

2. Some Tests of the k-Variable Linear Equation for Specification Error
- Specification error; Model evaluation and diagnostic tests; Tests of parameter constancy; Tests of structural change; Dummy variables

3. Maximum Likelihood (ML), Generalized Least Squares (GLS), and Instrumental Variable (IV) Estimators
- Maximum Likelihood estimators; ML estimation of the linear model; Likelihood ratio, Wald and Lagrange Multiplier Tests; Generalized Least Squares; Instrumental Variable estimators

4. Heteroscedasticity and Autocorrelation
- Properties of OLS estimators; Tests for heteroskedasticity and autocorrelation; Estimation under heteroskedasticity and with autocorrelated disturbances

PART 2: REGRESSION ANALYSIS WITH TIME SERIES DATA

5. Stationary univariate time series
- Univariate stochastic processes; ARMA models; autocorrelation and autocovariance functions; Wold's decomposition and invertible processes; Box-Jenkins selection

6. Modeling volatility
- ARCH and GARCH processes

7. Non-stationary univariate stochastic processes
- Models with trend; deterministic and stochastic trends; trend stationary and difference stationary series; integrated processes; unit root and stationarity tests
References:

8. Multivariate time series models with stationary regressors
- Autoregressive Distributed Lag (ADL) model; impact and long-run multipliers; impulse response function; Error correction model; Partial adjustment model

9. Multivariate time series models with integrated variables
- Linear combinations of integrated variables; spurios regressions; cointegration and ECM, testing for cointegration: Engle and Granger methodology

10. Multiple Equation Models
- Vector Autoregressions (VARs); Estimation of VARs; Vector Error Correction Models; Cointegration in VAR models; Johansen Methodology

PART 3: ADVANCED TOPICS

11. Panel data models
- Fixed and random effects models, correlated random effects, dynamic panel data models

12. Limited dependent variable models
- Linear probability model, Logit and Probit models, MLEs for binary choice model

Main references:

- Johnston, J. and J. Dinardo (1997), Econometric Methods, 4th edition, McGraw-Hill, New York.

- 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.
- Verbeek, M. (2017), A guide to modern econometrics, 5th Edition, Wiley,-

Additional references:

- Lectures slides made available on Moodle during the course

- Vogelvang B. (2005), Econometrics - Theory and Applications with EViews, FT Prentice Hall.
- Marcellino M. (2016), Applied Econometrics: An Introduction, EGEA.

Written discussion of the estimation results and analytical solutions of advanced econometric problems. Part of the final mark will depend on homework and an empirical project that can be handed in on a voluntary basis by the deadlines set by the instructor.

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.

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