COORDINATOR: Miguel A.
Delgado
DESCRIPTION:
This course offers
an introduction to data analysis in Social Sciences with the
assistance
of the multiple regression model. The emphasis is on the
interpretation
of the model and the application of statistical inference
techniques to
solve relevant practical problems. The course discusses in
detail how
to make inferences under non-standard situations, relevant in
Social
Sciences, due to the nature of the variables in the model
(qualitative,
transformed to allow nonlinear relations or non-observables)
or to the
nature of data. The rigorous formal justification of the
applied
statistical inference techniques is out of the scope of this
course.
The background in Probability, Statistics, Algebra and
Calculus offered
in Mathematics I & II and Statistics I & II is more
than enough
for this course.
At the end of the course, the student will
acquire a good working knowledge on the interpretation of the
linear
regression model, discriminating between alternative
specifications by
means of statistical inference, and using GRETL for estimation
and
hypothesis testing.
PRE-REQUISITES:
Some
knowledge of linear algebra, calculus and statistical theory
is
required, which is provided by Mathematics for Economics I
& II and
Statistics I.
SYLLABUS:
1. The nature of econometrics and economic data (SW. Ch. 1)
2. The simple regression model (SW. Ch. 4,5).
3. Multiple regression analysis: estimation (SW Ch. 6)
4. Multiple regression analysis: inference (SW. Ch. 7)
5. Nonlinear regression using linear multiple regression
(SW. Ch. 8).
6. Regression with binary dependent variable( SW. Cp.
11).
7. Instrumental variables estimation and two stages least
squares (SW. Cp. 12).
TEXTBOOKS AND ONLINE RESOURCES:
Stock,
J.H. and Watson, M.W., Introduction to Econometrics, Update, 3rd
Edition, Pearson, 2015.
Wooldridge, J.M., Introductory Econometrics, A Modern Approach, 2E, Addison Wesley, 2003.
REFERENCES:
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