APPLICATIONS OF ECONOMETRICS TO BIG DATA - 2023/4
Module code: ECOM076
The main application of machine learning is out-of-sample prediction. Prediction accuracy is typically evaluated in terms of squared error, where the error is difference between the prediction and the actual realization. In certain situations, such as forecasts of inflation or output from the Bank of England, an accurate prediction is enough. However, there are situations, like policy evaluation, in which we care about causal effects. Suppose that the Secretary of Education introduces three additional hours of mathematics in primary school to increase student GSE scores. Here the objective is to isolate the effect of additional hours of math on GSE score. In general, for each pupil, we have a lot of individual characteristics, which we need to control for. Data reduction techniques, such as LASSO, regression tree, random forest, help to eliminate all irrelevant information so that we can isolate the effect of the policy. This module is structured in two parts. In the first part, we review econometric tools for policy evaluation, such as instrumental variables, panel data, difference-in-difference, synthetic control, regression discontinuity. In the second part, we look at the same techniques when many instruments are available or many additional control variables are available.
MANDILARAS Alexandros (Economics)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 95
Lecture Hours: 22
Seminar Hours: 11
Captured Content: 22
Prerequisites / Co-requisites
Endogeneity and Instrumental Variables. Natural Experiments
Overview of Panel Data
Treatment Evaluation. Average and Local Average Treatment Effect
Synthetic Control Methods
Policy Evaluation in the presence of many instruments
Policy Evaluation on the presence of many control variables
|Assessment type||Unit of assessment||Weighting|
|Examination Online||Final Examination||70|
The assessment strategy is designed to provide students with the opportunity to demonstrate that they have achieved the module¿s learning outcomes.
Thus, the summative assessment for this module consists of:
A coursework assignment that allows students to undertake a policy evaluation exercise involving the use of big data by formalising a hypothesis of interest, selecting an appropriate data reduction and econometric estimation method, and writing their own code using a suitable software program
An examination that allows students to demonstrate a comprehensive understanding of and ability to evaluate critically methods of causal inference and policy evaluation in the possible presence of high-dimensionality data
Feedback Individual feedback will be provided on students¿ work during the weekly seminars and when coursework marks are released.
- Equip students with the econometric tools for conducting causal inference, and, so, for evaluating policy effects
- Enable students to undertake policy evaluation in the presence of high-dimensionality data (e.g., many instruments or many control variables)
|001||Display a systematic understanding of knowledge, which is at the forefront of the literature on big data techniques and policy evaluation||CK|
|002||Use competently econometric tools for policy evaluation in the presence of high-dimensionality data||CKPT|
|003||Formalize a hypothesis of interest and select appropriate data reduction and econometric estimation methods||CK|
|004||Write their own computer code using a suitable software program||CKPT|
|005||Use their newly acquired knowledge and skills to write an MSc Dissertation on a topic related to the econometrics of big data and policy evaluation||CKPT|
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 enable students to achieve the module¿s learning outcomes. There will be two hours of lectures and one hour of seminar every week. Problem sets based on the methodological topics taught during lectures and computer-based exercises will be reviewed during seminars/tutorials. Students are expected to work on an assignment and actively participate in the seminar hour.
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: ECOM076
Surrey's Curriculum Framework is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas: Resourcefulness and Resilience Developed through ability to solve problems under constraint and undertake applied work in the context of the coursework assignment. Digital Capabilities Through the ability to code using suitable software.
Programmes this module appears in
|Economics (Econometrics and Big Data) 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 2023/4 academic year.