Corsi di Studio del Dipartimento di Economia e Statistica "Cognetti de Martiis"

Statistics

Learning fundamentals of Econometrics, necessary to interpret multiple regression outputs, to plan and realize a simple empirical research project potentially useful for students to set up their final Master dissertation. Focus on empirical issues rather than theory. Bridge between the course of Statistics and more advanced courses such as Spatial Econometrics.

The student will be able to understand and handle basic tools of econometrics and will have the ability to use such techniques for the development of quantitative economic models.

Three mid-term take-home assignments on MyLab Economics.
Written final exam online on Webex.
Mid-term assignments and final exam content: multiple choice questions, empirical exercises, regression output interpretations.
More details on the exam grading system and other exam instructions can be found on the course Moodle page in the section "Final exam".

Office Hour:

During class months: on Wednesday h14-15; otherwise: on request by email to chiara.ardito@unito.it

The first part of the course will focus on cross-sectional data following a non-experimental approach. It will provide a deep insight on the linear regression model and ordinary least squares.

• Introduction to econometrics: economics questions and data (Chater 1)
• The linear regression model with one regressor (Ch. 4, 5)
  • Estimation
  • Measures of fit
  • Assumptions
  • Sampling distribution
  • Inference
  • Regression with binary regressor
  • Heteroskedasticity and homoscedasticity
• The linear regression with multiple regressor (Ch. 6, 7)
  • Omitted Variable Bias
  • Estimation
  • Measures of fit
  • Assumptions
  • Sampling distribution
  • Multicollinearity and dummy variable trap
  • Inference
• Multiple regression analysis: Further Issues (Ch. 8)
  • Logarithms
  • Quadratics and polynomials
  • Interactions between independent variables

In the second part of the course basics on limited dependent variables models, Poisson regression and time series data and analysis will be provided. Specifically: 

• Regression with binary dependent variable (Ch. 11)
  • Linear probability model
  • Probit and Logit model
  • Poisson Regression Model (class material to be provided)
• Time series Econometrics (Ch. 15)

Stock & Watson, Introduction to Econometrics plus MyLab Economics, Global Edition, 4^th edition, Pearson.

The course will be online on Webex.  Students should register to have access to the course. The course will start on the 4th November 2020.

Lectures time table:  Monday, Tuesday and Wednesday at 12:00-14:00 CET.