ADVANCED ECONOMETRICS 2 - 2022/3
Module code: ECOM066
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.
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.
CORRADI Valentina (Economics)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
Module cap (Maximum number of students): N/A
Overall student workload
Workshop Hours: 12
Independent Learning Hours: 98
Guided Learning: 22
Captured Content: 18
Prerequisites / Co-requisites
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
• Limited Dependent Variables
• Tobit Models
• Sample Selection
• Treatment Effect
• Forecast Evaluation
• Panel Data Models
• Nonlinear Panel Models
• Unbalanced Panel
• Missing Not at Random
|Assessment type||Unit of assessment||Weighting|
|Coursework||Coursework (Two Take Home Examinations)||30|
|Examination||ONLINE (OPEN BOOK) EXAM WITHIN 24HR WINDOW (TIMED)||70|
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
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.
- • 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
|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|
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 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.
Upon accessing the reading list, please search for the module using the module code: ECOM066
Programmes this module appears in
|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 2022/3 academic year.