ADVANCED ECONOMETRICS - 2017/8
Module code: MAND039
Surrey Business School
AGNEESSENS F Dr (SBS)
Number of Credits
FHEQ Level 8
Module cap (Maximum number of students)
Overall student workload
Lecture Hours: 20
|Assessment type||Unit of assessment||Weighting|
|Examination||FINAL EXAM PAPER||70|
Prerequisites / Co-requisites
The module provides the analytical tools we need 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 in finite sample, asymptotic approximations may be not accurate enough we study how to construct bootstrap critical values, in order to provide more accurate inference.
Given a problem of interest, the economist/econometrician needs to formalize it via a model. Models are approximations to reality and so are typically “wrong”, and this should be taken into account. Once we have collected the data, we use them to estimate our (possibly “wrong”) model, estimate parameters, test hypotheses and make predictions/forecasts. In order to do that in a sensible way, we need to make reasonable assumptions on our data, in terms of how much dependence and/or heterogeneity they display. Given these primitive assumptions, we need to derive the behaviour of our estimators or test statistics as the sample size gets large. Failing to do this correctly, leads us to construct invalid statistics, resulting in unreliable inference.
The module will provide the necessary analytical tools to become a competent and original user of econometric.
|Understand Econometrics papers in top general and top field journals||K|
|Formalize the hypotheses of interest||K|
|Modify existing tests/estimators to accommodate their own problems||K|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
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:
Try to involve student participation as much as possible.
Seminars will be student driven, with students presenting their solution to the blackboard.
The learning and teaching methods include:
2 hrs of lecture per week, plus one seminar every other week.
The assessment strategy is designed to provide students with the opportunity to demonstrate
Overall understanding of the material. Ability of connecting the various tools to solve a more general problem.
Thus, in line with domain A’s LO, the summative assessment for this unit consists of:
• Exercises (30%) enabling the students to show and practice their acquired knowledge;
• A final exam paper (70%) enabling the student to show their ability to apply their learning about advanced econometrics.
By-weekly feedback during seminar.
Reading list for ADVANCED ECONOMETRICS : http://aspire.surrey.ac.uk/modules/mand039
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 2017/8 academic year.