ADVANCED ECONOMETRICS 2 - 2018/9
Module code: ECOD006
CORRADI V Prof (Economics)
Number of Credits
FHEQ Level 8
Module cap (Maximum number of students)
Overall student workload
Independent Study Hours: 117
Lecture Hours: 22
Tutorial Hours: 11
|Assessment type||Unit of assessment||Weighting|
|Coursework||Coursework (Two Take Home Examinations)||30|
|Examination||Final Examination (3 hours)||70|
Prerequisites / Co-requisites
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.
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 papers on top econometric and statistical journal||KC|
|002||Evaluate the accuracy of competing models||KCT|
|003||Understand the basic tools for policy evaluation||KCT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Indicative content includes:
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
Panel Data Models
Nonlinear Panel Models
Missing Not at Random
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
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 three hour final examination
Two take home examinations, typically in weeks 6 and 10
Due to the limited size of the cohort and the level of study , formal formative assesment is replaced with informal discussions during and outside lectures.
Student will receive verbal feedback during the lectures and tutorials through direct interaction, as well more formally following coursework submission.
Reading list for ADVANCED ECONOMETRICS 2 : http://aspire.surrey.ac.uk/modules/ecod006
Programmes this module appears in
|Economics (Four Year) PHD||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 2018/9 academic year.