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Unit information: MRes Econometrics 2 in 2021/22

Unit name MRes Econometrics 2
Unit code EFIMM0022
Credit points 15
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Professor. Vincent Han
Open unit status Not open
Pre-requisites

MRes Econometrics I

Co-requisites

None

School/department School of Economics
Faculty Faculty of Social Sciences and Law

Description including Unit Aims

The purpose of this course is to develop the concepts needed to address empirical questions in microeconomics. The course mainly focuses on identification, estimation and inference problems in various nonparametric econometric models, especially for causal analyses. The first half will cover nonparametric estimation methods such as kernel and series estimations. It discusses the bias and variance trade-off in nonparametric estimation and the asymptotic properties of the estimators. This part be taught by Stefan Hubner. The second half of this course will cover various semiparametric and nonparametric microeconometric models, such as LDV models and models with endogenous regressors. The main focus is on identification. In particular, we develop a conceptual basis for understanding how data, econometric methodology and assumptions in combination produce statistical inference. We spend considerable time in thinking how to examine these assumptions and how critical they are in producing our results. This knowledge will be useful later in this course to explore more advanced topics in treatment effects and program evaluations.

Intended Learning Outcomes

1. The unit aims to build in students the ability to know, understand, and evaluate the various econometric tools and to apply them when undertaking novel research. Different methods invariably rely on different sets of assumptions, and after following the unit the student should be able to assess the plausibility of such assumptions before applying a method.

2. Moreover, the student must be able to grasp the intuition behind the workings of particular econometric methods, in order to assess their appeal and/or limitations and to develop ideas for methodological improvements.

3. The students should obtain sufficient foundational understanding of the topics to be able to access the corresponding journal articles first-hand and to evaluate them critically.

4. They should be able to start independent research projects with microeconometric data that require the methods taught, at basic levels.

Teaching Information

Teaching will be delivered through a combination of synchronous and asynchronous sessions such as online teaching for large and small group, face-to-face small group classes (where possible) and interactive learning activities

Assessment Information

Exam (100%)

Resources

If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.

If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. EFIMM0022).

How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours of study to complete. Your total learning time is made up of contact time, directed learning tasks, independent learning and assessment activity.

See the Faculty workload statement relating to this unit for more information.

Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit. The Board considers each student's outcomes across all the units which contribute to each year's programme of study. If you have self-certificated your absence from an assessment, you will normally be required to complete it the next time it runs (this is usually in the next assessment period).
The Board of Examiners will take into account any extenuating circumstances and operates within the Regulations and Code of Practice for Taught Programmes.

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