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 METHODS OF ECONOMIC INVESTIGATION 2: MICROECONOMETRICS
Code ECON312
Coordinator Dr Y Rao
Economics
Y.Rao@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2021-22 Level 6 FHEQ First Semester 15

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

ECON212 ECONOMETRICS 1; ECON213 ECONOMETRICS 2 

Modules for which this module is a pre-requisite:

 

Programme(s) (including Year of Study) to which this module is available on a required basis:

 

Programme(s) (including Year of Study) to which this module is available on an optional basis:

 

Teaching Schedule

  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours   7

  5

  24

12

48
Timetable (if known)       60 mins X 1 totaling 5
 
  120 mins X 1 totaling 24
 
 
Private Study 102
TOTAL HOURS 150

Assessment

EXAM Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
Assessment 1: Multiple Online tests Assessment Type: Written Exam Duration: 1 hour Weighting: 60% Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty applies  Multiple 1 hour test    60       
Assessment 2: Final test Written Unseen Examination Assessment Type: Written Exam Duration: 3 hours [expectation 2 hour completion time] Weighting: 40% Reassessment Opportunity: Yes Pen  3 hours [expectation    40       
CONTINUOUS Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
             

Aims

The module is designed to give students an introduction to the key advanced techniques in microeconometrics, building on second year work in econometrics. The module will develop applied research skills, including the ability to collect and analyse data using appropriate econometric techniques and bespoke software. The module will equip students for study at leading graduate schools of Economics and for work as professional economists/business analysts.


Learning Outcomes

(LO1) Explain the nature of and rationale for key techniques in microeconometrics

(LO2) Select and apply key microeconometrics techniques using appropriate econometric software

(LO3) Interpret and appraise econometric results arising from the application of key microeconometric techniques

(LO4) Conduct independent applied economic research

(S1) Problem Solving Skills

(S2) Numeracy

(S3) Organisational skills

(S4) IT skills

(S5) lifelong learning skills


Teaching and Learning Strategies

Teaching Delivery: Mixed, hybrid delivery with social distancing on campus.

Teaching Method: Online Asynchronous Learning Materials
Description: The 1 to 2-hour weekly online learning activities will give students a basic grounding in each of the key topic areas as well as demonstrating how techniques are applied and how to interpret results. Module leader would also provide guidance on activity preparation and assessment
Unscheduled Directed Student Hours: 24
Attendance Recorded: Yes

Teaching Method: Seminars
Description: The objective is to give students an opportunity to participate learning activities and also to clarify areas of uncertainty regarding the module material and module assessments.
Scheduled Directed Student Hours: 7
Attendance Recorded: Yes

Teaching Method: Lab Seminar
Description: The computer-based seminars will give students practice in using bespoke econometric software to generate results and to exp lore further how those results should be assessed and interpreted.
Scheduled Directed Student Hours: 5
Attendance recorded: Yes
Notes: weeks 3, 5, 8, 10 and 12

Teaching Method: Group Study
Description: Weekly 1 hour session to foster student community and engagement by working with others on their ‘active learning’ activities
Scheduled Student Hours: 12
Attendance recorded: No

Self-Directed Learning Hours: 102
Description: These independent learning hours are aimed at supporting the directed student learning. The module leader will provide guidance in the form of suggested readings and topics to examine with the expectation that students are well prepared to understand the content of learning materials. Self-Directed Learning will include review learning materials, prepare for seminar activities, and wider reading to support the module

Skills/Other Attributes Mapping

Skills / attributes: lifelong learning skills
How this is developed: tutorials, self-study
Mode of assessment (if applicable)

Skills / attributes: IT skills
How this is developed: tutorials
Mode of assessment (if applicable)

Skills / attributes: Organisational skills
How this is developed: Lectures, tutorials and self-study
Mode of assessment (if applicable): Examination
Skills / attributes: Teamwork
How this is developed: tutorials
Mode of assessment (if applicable): Examination

Skills / attributes: Numeracy
How this is developed: Lectures, tutorials
Mode of assessment (if applicable): Examination

Skills / attributes: Problem Solving Skills
How this is developed: Lectures, tutorials
Mode of assessment (if applicable): Examination


Syllabus

 

Review of the standard multiple regression model
Principles of maximum likelihood estimation
Models for binary dependent variables to explain either/or outcomes (e.g. whether or not to participate in the labour force; acceptance or refusal of credit): linear, probit and logit
Multinomial logit models to explain outcomes where there are more than two possibilities (e.g. choice of occupation, which brand of car to buy)
Models for explaining outcomes which can be ranked (e.g. which class of degree will a student obtain, analysis of consumer surveys): ordered probit and logit
Count data models (e.g. how many televisions will a household own, how many patents will a firm obtain, how many accidents will happen)
Seemingly unrelated regression and introduction to panel data methods (combining time series and cross-section data - applications to modelling investment)
Fixed effects panel data methods (application to modelling earnings)
Random effects pa nel data methods (modelling earnings and comparison with fixed effects)
Model selection, model building.


Recommended Texts

Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.