ECONOMETRICS 2 - 2019/0
Module code: ECOM043
This 15 credit module introduces students to the econometric techniques relevant for the estimation of a range of models including those with either qualitative or limited dependent variables, models for survival analysis, and panel data models. A strong emphasis is placed on the empirical applications of these techniques and their intuition
WITT Robert (Economics)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: L140
Module cap (Maximum number of students): N/A
Overall student workload
Prerequisites / Co-requisites
ECOM042 (Econometrics 1) is a co-requisite for this module
Indicative content includes:
- Maximum Likelihood Principle, LP and Logit, Probit Models
- Truncated and Censored Regression Models, Duration Models
- Panel Data Models
|Assessment type||Unit of assessment||Weighting|
|School-timetabled exam/test||IN-SEMESTER TEST - 1 HOUR||25|
|Examination||EXAMINATION - 2 HOUR||75|
The assessment strategy is designed to provide students with the opportunity to demonstrate the students’ understanding of the statistical concepts associated with the methods studied, and ability to use STATA to solve simple problems can be assessed
Thus, the summative assessment for this module consists of:
- 2 hour unseen examination paper (75%) scheduled in Weeks 13-15. Statistical tables are provided for this assessment and candidates are permitted to use a pocket calculator.
- 1 hour multiple choice test in Week 7 worth 25%.
Formative assessment and feedback
Class test plus a two hour exam
Students will receive verbal feedback on their performance during the lab workshops and in the lectures.Office hours provide further opportunities for individual feedback.
- access and understand an applied economics literature that uses these techniques
- undertake competent empirical research using such techniques and
- develop a familiarity with the STATA econometric package in undertaking such empirical analysis.
|001||Demonstrate a systematic understanding of limited dependent variables methods and to apply them to simple situations||KC|
|002||Deal with complex concepts used for survival analysis and panel data models and to apply them to simple situations||KC|
|003||Use the Stata software package to develop further their understanding of the syllabus material||PT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
- Make the students understand the ideas behind the methods analysed;
- Allow the students to apply these methods to simple situations.
- Reading using lecturers guidance
- Preparing exercises for discussion in computing workshops
- Responding to questions in lectures and in computing workshops
The learning and teaching methods include:
- 11 x 2 hour lectures
- 8 x 1 hour STATA lab workshops
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: ECOM043
Programmes this module appears in
|Business Analytics MSc||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Economics MSc||2||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|International Economics, Finance and Development MSc||2||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Economics and Finance MSc||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Business Economics and Finance MSc||2||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2019/0 academic year.