A86050 Quantitative Methods for Economics, Finance and Management

Scuola di Economia e Management
Syllabus
Academic Year 2017/18 First Semester

foto
Docente TitolareMatteo Manera
E-mailmmanera@liuc.it
Office"Torre" (main tower), 4th floor
Phone

Learning Objectives

This course is designed to teach methods of data analysis to students whose primary interest is not in econometrics, statistics or mathematics. It purports to show students how to apply econometric techniques in the context of real-world empirical economic and financial problems. It covers most of the tools used in modern econometrics research, e.g. correlation, regression and extensions for time-series methods. During the course extensive use of real data examples is made and students are involved in hands-on computer work.

Learning targets

Students will be able to analyze economic data and the relationships among economic variables by using appropriate statistical tools.

Course Content

Descriptive statistics; Correlations; Regression analysis with one explanatory variable; Hypothesis testing; Regression analysis with multiple explanatory variables; Dummy variables as explanatory variables; Heteroskedasticity; Time series analysis and autocorrelation; Stochastic regressors and instrumenat variables.

Course Delivery

Course Evaluation

The assessment will be based on a written final examination. During the course, students will be encouraged to solve theoretical as well as empirical exercises, individually and in small groups.

 

Syllabus

Session 1
Hours of lesson: 4
Instructor: M. Galeotti

Topics:

Introduction to econometrics. Economic questions and data.

Readings:

Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapter 1.

Session 2
Hours of lesson: 4
Instructor: M. Galeotti

Topics:

Introduction to econometrics. Review of probability and statistics.

Readings:

Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapters 2-3.

Session 3
Hours of lesson: 4
Instructor: M. Galeotti

Topics:

Linear regression model with a single explanatory variable.

Readings:

Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapter 4.

Session 4
Hours of lesson: 4
Instructor: M. Galeotti

Topics:

Hypothesis testing and confidence intervals in the context of the linear regression model with a single explanatory variable.

Readings:

Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapter 5.

 

Session 5
Hours of lesson: 4
Instructor: M. Manera

Topics:

Linear regression model with a single explanatory variable: empirical applications.

Readings:

Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapter 4.

Session 6
Hours of lesson: 4
Instructor: M. Manera

Topics:

Linear regression model with a single explanatory variable: empirical applications.

Readings:

Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapter 4.

Session 7
Hours of lesson: 4
Instructor: M. Manera

Topics:

Linear regression model with a single explanatory variable: empirical applications.

Readings:

Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapter 4.

Session 8
Hours of lesson: 4
Instructor: M. Manera

Topics:

Hypothesis testing and confidence intervals in the context of the linear regression model with a single explanatory variable: empirical applications.

Readings:

Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapter 5.

Session 9
Hours of lesson: 4
Instructor: M. Manera

Topics:

Hypothesis testing and confidence intervals in the context of the linear regression model with a single explanatory variable: empirical applications.

Readings:

Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapter 5.

Session 10
Hours of lesson: 4
Instructor: M. Manera

Topics:

Hypothesis testing and confidence intervals in the context of the linear regression model with a single explanatory variable: empirical applications.

Readings:

Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapter 5.

 

Session 11
Hours of lesson: 3,5
Instructor: M. Galeotti

Topics:

Linear regression model with multiple explanatory variables: hypothesis testing, dummy variables.

Readings:

Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapters 6-7.

Session 12
Hours of lesson: 3
Instructor: M. Manera

Topics:

Hypothesis testing and dummy variables in the context of the linear regression model with multiple explanatory variables: empirical applications.

Readings:

Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson., Chapters 6-7.

Session 13
Hours of lesson: 3,5
Instructor: M. Galeotti

Topics:

Linear regression model with multiple explanatory variables: heteroskedasticity.

Readings:

Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapters 6-7.

Session 14
Hours of lesson: 3,5
Instructor: M. Galeotti

Topics:

Linear regression model with multiple explanatory variables: autocorrelation, time series analysis, forecasting.

Readings:

Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson; Chapter 14.

 

Session 15
Hours of lesson: 3,5
Instructor: M. Galeotti

Topics:

Linear regression model with multiple explanatory variables: stochastic regressors and instrumental variables.

Readings:

Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapter 12.

Session 16
Hours of lesson: 3
Instructor: M. Manera

Topics:

Autocorrelation, heteroskedasticity and instrumental variables: empirical applications.

Readings:

Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapters 6-7, 12, 14.

 

 


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