ADVANCED ECONOMETRICS 2 - 2021/2
Module code: ECOM066
In light of the Covid-19 pandemic, and in a departure from previous academic years and previously published information, the University has had to change the delivery (and in some cases the content) of its programmes, together with certain University services and facilities for the academic year 2020/21.
These changes include the implementation of a hybrid teaching approach during 2020/21. Detailed information on all changes is available at: https://www.surrey.ac.uk/coronavirus/course-changes. This webpage sets out information relating to general University changes, and will also direct you to consider additional specific information relating to your chosen programme.
Prior to registering online, you must read this general information and all relevant additional programme specific information. By completing online registration, you acknowledge that you have read such content, and accept all such changes.
Module Overview
The module builds up over the material covered in Advanced Econometrics 1. When the correct functional form is unknown one relies on nonparametric techniques, such as kernel techniques. This module involves the advanced study of the asymptotic properties as well as the practical implementation of nonparametric regression. This is followed by an overview of the main tools used in Time Series Analysis, which provides the basis for the analysis of macroeconomic and financial series. Finally, the module also provides the statistical tools used in Microeconometrics. Binary Choice Models, in the standard case and in the presence of endogeneity. Also to limited dependent variables, with special focus on Tobit models and sample selection.The module concludes with the study of panel data, including the most recent developments such as nonlinear panel models and endogenous attrition.
Module provider
Economics
Module Leader
CORRADI Valentina (Economics)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code:
Module cap (Maximum number of students): N/A
Module Availability
Semester 2
Prerequisites / Co-requisites
None
Module content
Indicative content includes:
• Nonparametric Estimators
• Density Estimation: Bias and Variance
• Consistency of Conditional Mean Estimators
• Asymptotic Normality and Rates of Convergence
• Issues in Implementing Nonparametric Regression
• Binary Choice Models
• Probit and Logit
• Endogeneity
• Limited Dependent Variables
• Tobit Models
• Sample Selection
• Treatment Effect
• Forecast Evaluation
• Panel Data Models
• Nonlinear Panel Models
• Unbalanced Panel
• Missing Not at Random
Assessment pattern
| Assessment type | Unit of assessment | Weighting |
|---|---|---|
| Coursework | Coursework (Two Take Home Examinations) | 30 |
| Examination | Final Examination (2 hours) | 70 |
Alternative Assessment
None.
Assessment Strategy
The assessment strategy is designed to provide students with the opportunity to demonstrate their technical skills relating to the use of econometrics techniques to do innovative empirical work.
Thus, the summative assessment for this module consists of:
A two hour final examination
Two take home examinations, typically in weeks 6 and 10
Formative assessment
Discussions during and outside lectures. Feedback Student will receive verbal feedback during the lectures and tutorials through direct interaction, as well more formally following coursework submission.
Module aims
- • provide the advanced tools required to become competent and creative users of econometrics.
- • enable students to combine existing tools so as to find novel ways of solving econometrics problems.
- • enable students to undertake independent research in econometrics
Learning outcomes
| Attributes Developed | ||
|---|---|---|
| 001 | Understand and interpret in a critical way paoers on top econometric and statistical journal | CK |
| 002 | Evaluate the accuracy of competing models | CKT |
| 003 | Understand the basic tool for policy evaluation | CKT |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Overall student workload
Independent Study Hours: 117
Lecture Hours: 22
Tutorial Hours: 11
Methods of Teaching / Learning
The learning and teaching strategy is designed to: develop student independent research skills, by training them to do critical analysis of papers in scientific journals. Problems set will assigned to ensure all concepts and methods are properly mastered.
The learning and teaching methods include:
• Interactive lectures. Review of problem sets solution
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.
Reading list
https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: ECOM066
Other information
None.
Programmes this module appears in
| Programme | Semester | Classification | Qualifying conditions |
|---|---|---|---|
| Economics MRes | 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 2021/2 academic year.