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JOHN CABOT UNIVERSITY

COURSE CODE: "EC 360"
COURSE NAME: "Econometrics "
SEMESTER & YEAR: Fall 2017
SYLLABUS

INSTRUCTOR: Davide Ciferri
EMAIL: [email protected]
HOURS: MW 7:30-8:45 PM
TOTAL NO. OF CONTACT HOURS: 45
CREDITS:
PREREQUISITES: Prerequisites: EC 201, EC 202, MA 209
OFFICE HOURS:

COURSE DESCRIPTION:
Econometrics is the use of statistical tools to test economic models. This course will introduce students to the basic principles of econometrics and will provide them with hands-on practical experience in the field. The course starts with a review of statistical tools and continues with the analysis of simple and multiple regression, heteroskedasticity, autocorrelation, and multicollinearity. Some of the teaching time will be spent in the computer lab, where students will learn how to work with software.
SUMMARY OF COURSE CONTENT:

Single-equation regression model, classical normal linear regression model, statistical inference, hypothesis testing,

misspecification, autocorrelation, heteroscedasticity, multicollinearity, dummy variable regression models, qualitative

response regression models.

LEARNING OUTCOMES:

Students should gain a solid foundation in both theoretical and empirical econometrics. and the basic skills needed to

develop an empirical research.

TEXTBOOK:
Book TitleAuthorPublisherISBN numberLibrary Call NumberComments
Basic EconometricsDamodar Gujarati and Dawn Porter McGraw-Hill978-0073375779  
REQUIRED RESERVED READING:
NONE

RECOMMENDED RESERVED READING:
NONE
GRADING POLICY
-ASSESSMENT METHODS:
AssignmentGuidelinesWeight
EXAM IReview of some Statistics and mathematical tools, Single and Two variable Regression Model15%
EXAM IIMultiple Regression Analysis and Hypothesis Testing15%
EXAM IIIDummy variables, Multicollinearity, Heteroscedasticity, Autocorrelation15%
PROJECTEstimation and analysis of empirical results using econometric software15%
FINAL 40%

-ASSESSMENT CRITERIA:
AWork of this quality directly addresses the question or problem raised and provides a coherent argument displaying an extensive knowledge of relevant information or content. This type of work demonstrates the ability to critically evaluate concepts and theory and has an element of novelty and originality. There is clear evidence of a significant amount of reading beyond that required for the co
BThis is highly competent level of performance and directly addresses the question or problem raised.There is a demonstration of some ability to critically evaluatetheory and concepts and relate them to practice. Discussions reflect the student’s own arguments and are not simply a repetition of standard lecture andreference material. The work does not suffer from any major errors or omissions and provides evidence of reading beyond the required assignments.
CThis is an acceptable level of performance and provides answers that are clear but limited, reflecting the information offered in the lectures and reference readings.
DThis level of performances demonstrates that the student lacks a coherent grasp of the material.Important information is omitted and irrelevant points included.In effect, the student has barely done enough to persuade the instructor that s/he should not fail.
FThis work fails to show any knowledge or understanding of the issues raised in the question. Most of the material in the answer is irrelevant.

-ATTENDANCE REQUIREMENTS:
Strongly encouraged
ACADEMIC HONESTY
As stated in the university catalog, any student who commits an act of academic dishonesty will receive a failing grade on the work in which the dishonesty occurred. In addition, acts of academic dishonesty, irrespective of the weight of the assignment, may result in the student receiving a failing grade in the course. Instances of academic dishonesty will be reported to the Dean of Academic Affairs. A student who is reported twice for academic dishonesty is subject to summary dismissal from the University. In such a case, the Academic Council will then make a recommendation to the President, who will make the final decision.
STUDENTS WITH LEARNING OR OTHER DISABILITIES
John Cabot University does not discriminate on the basis of disability or handicap. Students with approved accommodations must inform their professors at the beginning of the term. Please see the website for the complete policy.

SCHEDULE

SessionSession FocusReading AssignmentOther AssignmentMeeting Place/Exam Dates
Week 1Review of some Statistics and Mathematical toolsReview of some Statistics and Mathematical tools: Appendix A, B, C  
Week 2Simple Regresssion ModelChapter 2  
Week 3Multiple Regression Analysis: EstimationChapter 3  
Week 4Review and First Exam  09/21/2017
Week 5Multiple Regression Analysis: InferenceChapter 4  
Week 6Multiple Regression Analysis: Further IssuesChapter 5, 6  
Week 7Review and Second Exam   
Week 8Computer LAB  10/19/2017
Week 9Dummy Variable Regression ModelChapter 7  
Week 10HeteroskedasticityChapter 8  
Week 11Time Series Model, MutlicollinearityChapter 10  
Week 12AutocorrelationChapter 12  
Week 13Review & Third Exam  11/23/2017
Week 14Computer LAB & Individual Project