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 2023-24 Level 6 FHEQ First Semester 15

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

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 24

 6

         30
Timetable (if known)              
Private Study 120
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Assessment 2: Written Unseen Examination Assessment Type: Written Exam Duration: 2 hours Weighting: 80% Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty a  2    80       
Assessment 1: Midterm Assessment Type: Written Exam Duration: 1 hour Weighting: 20% Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty applies Anonymous A  1    20       
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 Method: Lecture
Scheduled Directed Student Hours: 24
Attendance Recorded: No

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: 6
Attendance Recorded: Yes

Self-Directed Learning Hours: 120
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: t utorials, 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.