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Elementi di econometria (in lingua inglese)
Riccardo Lucchetti

Seat Fac. Economia - Sede di Ancona
A.A. A.A. 2016-2017
Credits 6
Hours 44
Period 1^ semestre
Language ENG

Prerequisites

Attending the course borders on the useless if the student does not have a working knowledge of basic statistical principles and methods.

Development of the course

The course will involve traditional lectures with theory content as well as computer practice sessions using the gretl software. The proportion between the two kinds will be about 3 to 1.

Learning outcomes

1.   Knowledge and Understanding
This course will give the students a basic knowledge of the main econometric techniques. Nevertheless, a contemporary and rigorous approach will be used, based on projection operators an almost entirely on asymptotic inference without the normality assumption. This will provide students with a solid grounding if they choose to further pursue the study of Econometrics.
2.    Applying knowledge and  understanding 
Ability to apply the linear model for estimation and testing of models of conditional expectation.
3.    Judgement, focus  and  communication skills
Either the theory elements that will be brought to the attention of the students during the course and the practice sessions will enable the students to develop a decent awareness on the main econometric methods used in business, economic and financial problems. Students will improve their proficiency in the creation, maintenance and usage of economic data files and their analysis via specialised software packages.

 

Program

1.    Contents (lectures, around 33 hours)
The linear model: descriptive and geometrical properties: basic linear algebra: vector spaces, linear combinations, projections. OLS as solution of a minimisation problem. Properties of the orthogonal projection matrices Px and Mx.
inferential interpretation of the linear model: review of basic probability theory (random variables, independence and conditioning) and statistical inference (properties of estimators, especially asymptotic; LLN and CLT); linear model as a model for conditional expectation. Specification tests.
Diagnostics in the linear model: tests of mis-specfication for the conditonal expectation (RESET, Chow); heteroskedasticity and autocorrelation. Cursory treatment of robust inference.
2.    Practice sessions (with PC, around 11 hours)
Usage of gretl as a “matrix calculator”. Introduction to basic programming (assignment, output, conditional branching, loops). Introduction to basic dataset management. Example of estimation and interpretation of linear models on real datasets.

 

Development of the examination

1.   Exam. 
The final exam is a written test, including 5 multiple choice questions on general mathematical/statistical topics, an exercise on estimation and testing and a comment on figures based on some real data set. The maximum score for each question is 10 points.
2.    Assessment criteria.
Students will be required to have acquired a working knowledge of the principles and methods on estimation and hypothesis testing in linear models. Students are also expected to show the ability of discussing and interpreting the quantitative results thus obtained.
3.    Grading scale 
The final mark is in 30ths (minimum 18). Possibly, a special mention (cum laude) can be awarded.
4.    Grading method 
The final mark will be the by summing the points achieved in each question.A special mention (cum laude) will be awarded to students who display a critical and comprehensive understanding of the course contents.

 

Recommended reading

Hansen, Econometrics, available at http://www.ssc.wisc.edu/~bhansen/econometrics/, chapters 2-8 (selected parts)
Verbeek, M.  (2012) “A Guide to Modern Econometrics” (4th Edition), Wiley (selected parts)

 

Courses

• L.T. - Economia e Commercio n.o. Sede Ancona