ADVANCED ECONOMETRICS 1 - 2018/9
Module code: ECOD003
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|
|School-timetabled exam/test||TAKE HOME TEST||30|
Prerequisites / Co-requisites
The module provides the analytical tools required for deriving the limiting distribution of estimators in the context of linear models (OLS and instrumental variables) and nonlinear models (NLS and Generalized Method of Moments). Since asymptotic approximations may be not be sufficiently accurate in finite samples, we study how to construct bootstrap critical values, in order to provide more accurate inference.
Provide students with an advanced understanding of key statistical and econometric tools
Enable student to state a hypothesis of interest and derive a test
Enable students to be competent and innovative econometrics users
|001||Understand econometrics papers in top general and top field journals.||KC|
|002||Formalise hypotheses of interest||KCT|
|003||Modify existing tests/estimators to accommodate their own econometrics problems||KCT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Indicative content includes:
Statistics tools: Modes of Convergence, Law of Large Numbers, Central Limit Theorems
Consistency and Asymptotic Normality of Ordinary Least Squares Estimators
Hypothesis Testing: Wald, Lagrange Multiplier and Likelihood Ratio Tests
Estimation of Asymptotic Covariance Matrices
Instrumental Variables Estimators: (1) Consistency and Asymptotic Normality, (2) Weak instruments and weak identification
Consistency and Asymptotic Normality of Generalized Method of Moments Estimators (GMM), Tests for Identifying Restrictions
The Bootstrap and its Applications
Methods of Teaching / Learning
The learning and teaching strategy is designed to: develop student independent research skills, by training them to conduct critical analysis of papers in scientific journals. Problems sets will be assigned to ensure all concepts and methods are properly mastered.
The learning and teaching methods include:
Interactive lectures. Review of problem set solutions.
The assessment strategy is designed to provide students with the opportunity to demonstrate their technical skills relating to the use of econometrics techniques to conduct innovative empirical work.
Thus, the summative assessment for this module consists of:
A three- hour final examination
A take home examination involving matlab programming and theoretical exercises, typically due in week 9.
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 1 : http://aspire.surrey.ac.uk/modules/ecod003
Programmes this module appears in
|Economics (Four Year) PHD||1||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.