TOPICS IN APPLIED ECONOMETRICS - 2022/3
Module code: ECO3010
In light of the Covid-19 pandemic the University has revised its courses to incorporate the ‘Hybrid Learning Experience’ in a departure from previous academic years and previously published information. The University has changed the delivery (and in some cases the content) of its programmes. Further information on the general principles of hybrid learning can be found at: Hybrid learning experience | University of Surrey.
We have updated key module information regarding the pattern of assessment and overall student workload to inform student module choices. We are currently working on bringing remaining published information up to date to reflect current practice in time for the start of the academic year 2021/22.
This means that some information within the programme and module catalogue will be subject to change. Current students are invited to contact their Programme Leader or Academic Hive with any questions relating to the information available.
This module builds on the econometrics foundation from the Introductory Econometrics and Intermediate Econometrics courses. The emphasis of this module is to introduce econometric techniques used to analyse microeconomic data. The first half of this module considers techniques to achieve causal inference. The second half studies maximum likelihood estimation of limited dependent variables models (logit, probit, Poisson, censoring, and selectivity), as well as basic machine learning methods.
BLANDEN Jo (Economics)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 6
JACs code: L140
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 84
Lecture Hours: 22
Guided Learning: 11
Captured Content: 33
Prerequisites / Co-requisites
The course covers models used in the policy evaluation literature (RCTs, regression with covariates, difference-in-differences and regression discontinuity design), maximum likelihood estimation of limited dependent variables models (logit, probit, Poisson, censoring, truncation and selectivity) and basic machine learning methods.
|Assessment type||Unit of assessment||Weighting|
The assessment strategy is designed to provide students with the opportunity to demonstrate:
Their understanding of econometric methods beyond simple linear regression framework that are commonly used in analysing microeconomic data, and the ability to use relevant computer packages to investigate real world economic problems.
Thus, the summative assessment for this module consists of:
- Mid-term computer-based class test, worth 30% of the final mark.
- Final written exam containing questions covering all 11 weeks. Worth 70% of the final mark. The exam has two sections. Section A contains four questions for students to choose three. Section B contains three questions for students to choose two. The allocation of questions will take into account that the materials in the first 5 weeks have partially been examined in the mid-term exam.
Formative assessment and feedback
Students receive verbal feedback during lectures through direct questioning (in which multiple questions and real-world examples of the use of economics are discussed). There are also homework assignments throughout the course, where feedback is provided for all individual questions.In addition to this, they receive guidance and illustrations to the use of Stata.
- Equip the student with the ability to undertake, understand, and critically assess empirical work in economics, with a view to enabling the student to use micro-econometrics to catalogue and describe empirical regularities and test various propositions.
|001||Use Stata to analyse microeconomic datasets||KCPT|
|002||To understand models used for causal inference including regression, randomised control trials, difference in differences and regression discontinuity designs.||KCP|
|003||To be able to apply the knowledge gained in (2) to a variety of real world research contexts.||KPT|
|004||To gain familiarity with modelling limited dependent variables, in particular models for corner solutions, counts, and binary outcomes. Understand the concepts of censoring, corner solutions, truncation, sample selection, and their relationship.||KCPT|
|005||Gaining a basic understanding of machine learning methods.||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:
Develop skills in modelling economic problems empirically and use computer packages to estimate and test various propositions
Appreciate the intuition behind different econometric methods applied in different situations (theory and practice)
The learning and teaching methods include:
1 hour workshop per week plus one hour captured content x 6 weeks
2 hour lecture / lab per week x 5 weeks
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: ECO3010
Programmes this module appears in
|Economics and Finance BSc (Hons)||2||Optional||A weighted aggregate mark of 40% is required to pass the module|
|Economics BSc (Hons)||2||Optional||A weighted aggregate mark of 40% is required to pass the module|
|Economics and Mathematics BSc (Hons)||2||Optional||A weighted aggregate mark of 40% 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 2022/3 academic year.