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◄Faculty of Economics and Administration►

(2) Evaluate critically the application of different statistical
methods in examining a statistical problem of interest;
(3) Synthesize the information from the relevant literature
for examining a statistical problem of interest; and
(4) Plan, manage and prepare the project paper in a
timely manner.
Synopsis of Course The main objective of this course is to explore the
Contents application of various statistical methods in data analysis
through the evaluation of a number of articles. The course
exposes students to efficient literature search. The focus is
on a statistical problem of interest. Through the critical
evaluation of journal articles and other works, the student
will be able to gain a greater understanding about the
various statistical methods used in the analysis of data.
Students will be guided in searching for, identifying,
summarizing and managing the necessary reading
materials.
Assessment Continuous Assessment: 100%
Main Reference (1) Cooper, Harris. Synthesizing Research: A Guide for
Literature Reviews, 3rd ed. (Applied Social Research
Methods Series, v. 2) Thousand Oaks, Calif: Sage
Publications, 1998.
(2) Galvan, Jose L. Writing Literature Reviews: A Guide
for Students of the Social and Behavioral Sciences.
Los Angeles, CA: Pyrczak, 1999.

ESGC6356 APPLIED ECONOMETRICS

Learning Outcomes At the end of the course, students are able to:

(1) Apply regression analysis for quantifying economic
relationships;
(2) Construct models and formulate hypotheses in a
manner suitable for econometric testing;
(3) Appraise the adequacy of regression models
estimated using econometric software;
(4) Draw valid conclusions from the results of estimation
and hypothesis-testing; and
(5) Evaluate the performance of alternative econometric
models through appropriate tests.

Synopsis of Course The course is designed to equip students with
Contents econometric tools of analysis for research work.
Computer software is used for the purposes of
estimation, prediction and basic modelling. Single-
equation models in the classical context are given
emphasis. Diagnostic tests and problems of estimation
(multicollinearity, heteroscedasticity and autocorrelation)
are discussed. Extensions to single-equation models
covered include qualitative choice models, dummy
variables and autoregressive and distributed lag model.
Introduction to simultaneous-equation models is given.

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