ECONOMETRICS

The course aims to handle some aspects of the econometric methods with respect to the regression models, both univariate and multivariate. The focus is on cross sections and time series data. In particular, the main concepts of exogeneity, stationarity and cointegration are introduced. Consequently, the purpose is to prepare the student to use the basic econometric tools for the measurement, interpretation and forecast of the economic and financial phenomena. The course is well equipped with econometric practice.

Knowledge and competences
Attendance and active participation in lectures, online activities, exercise sessions, tutoring activities, together with the individual study will allow the student to acquire the following knowledge and understanding skills:
- sound knowledge of the theoretical foundations of econometric models and methods
- specification, estimation and forecasting with regression models
- investigate, understand and interpret economic and financial phenomena, by means of up-to-data econometric tools

Application of acquired knowledge and skills
Through the interaction with the instructors, the tutors, and peers and through the individual study the student acquires the following abilities:
- ability to exploit up-to-date analytical tools and formal derivations to gain insights on relevant economic relationships
- treatment of macroeconomic and financial data for the specification, estimation and forecasting with regression models
- ability to analyze current aspects of the real and financial economy, using time series downloaded from available databases

Judgement and interpretation skills
Regarding the autonomy of judgment, communication skills and learning abilities, through the personal and group study of the concepts seen in class, the student will be able to:
- interpretation and management of economic dynamics, through the use of advanced analytical tools
- interpretation of the results produced by an econometric software
- evaluate strengths and weaknesses of the methodologies analyzed and of their empirical application
- being able to critically interpret the outcomes of empirical analyses

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

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

Italian

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