ULMS Electronic Module Catalogue
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title BASIC ECONOMETRICS 1
Code ECON212
Coordinator Dr M Stamatogiannis
Economics, Finance and Accounting
M.Stamatogiannis@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2016-17 Level 5 FHEQ First Semester 15

Pre-requisites before taking this module (other modules and/or general educational/academic requirements):
ECON111; ECON112 ECON111 and ECON112  

Modules for which this module is a pre-requisite:
ECON213; ECON308; ECON311; ECON312 

Programme(s) (including Year of Study) to which this module is available on a required basis:
Programme:L100 Year:2 Programme:GL11 Year:2 Programme:G1N3 Year:3 Programme:L101 Year:2

Programme(s) (including Year of Study) to which this module is available on an optional basis:
Programme:GN11 Year:2 Programme:L000 Year:2 Programme:Y001 Year:2 Programme:G1N2 Year:2

Teaching Schedule
  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 24

     5

     29
Timetable (if known)              
Private Study 121
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Unseen Written Exam  2 hours  First  70  Yes    Assessment 2 Notes (applying to all assessments) Mid-term Test Resit via a piece of coursework Unseen, closed book exam  
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Coursework  One hour  1  30  Yes  Standard UoL penalty applies  Assessment 1 

Aims

  • Econometrics is concerned with the testing of economic theory using real world data. This module introduces the subject by focusing on the principles of Ordinary Least Squares regression analysis. The module will provide practical experience via regular laboratory session.

     

  • This module also aims to equip students with the necessary foundations in econometrics to successfully study more advanced modules such as ECON213 Basic Econometrics II, ECON311  Methods of Economic Investigation: Time Series Econometrics and ECON312 Methods of Economic Investigation 2: Microeconometrics.

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    Learning Outcomes
    Reinforce the  understanding of fundamental principles of statistics, probability and mathematics to be used in the context of econometric analysis

    Estimate simple regression models with pen and paper using formulae and with the econometric software EViews7

    Understand the assumptions underpinning valid estimation and inference in regression models

    Formulate and conduct intervals of confidence and tests of hypotheses

    Evaluate the impact that changes in the unit of accounts of variables and changes in the functional form of equations may have upon the results of OLS and their interpretation

    Assess the goodness of results by means of appropriate tests and indicators

    Assess predictions

    Extend analysis to the context of multiple linear regression

    Use EViews7 to estimate simple linear regression models  and multiple linear regression models


    Teaching and Learning Strategies

    Lecture -

    Laboratory Work -


    Syllabus
    1  1

    Introduction: The Nature of Econometrics Concept of Causality and Ceteris Paribus

    Revision of Basic Concepts in statistics, mathematics and probability: summation, random variables, expected value (mean) and variance

    Revision of Sample estimators (average/variance) and distributions (normal, standardized normal, T-Student, F and Chi-square)

    The Simple Linear Regression Model – Least Squares (LS) Estimation

    Properties of LS Estimators and LS assumptions

    Interval of confidence and Hypothesis Testing in the Simple Linear Regression Model 

    Prediction and goodness of Fit

    Functional form and magnitude of variables

    Introduction to the estimation of the Multiple Linear Regression Model

     


    Recommended Texts
    Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.
    Explanation of Reading List: