25922 Financial Econometrics

Subject level: Undergraduate

Handbook description

This subject extends knowledge of financial econometrics and model building to enable comprehension of advanced research literature and confident use of econometric techniques in research. Topics include: maximum likelihood estimation and inference in linear and nonlinear models; modern time series methods of dealing with integrated variables; modelling volatility with the ARCH class of models; and econometrics packages.

Subject objectives/outcomes

On successful completion of this subject, students should be able to:

1. derive econometric estimators theoretically, and to explain their properties. Students will be able to predict how these properties change in response to violations of the standard assumptions of regression analysis. Students will also know how to match the estimation technique to the data generating environment (i.e. a times series, cross sectional, or panel environment)
2. gain a good understanding of Ordinary Least Squares (OLS) using: intuition, graphs, algebra and matrix algebra
3. generate empirical models in EVIEWs, and correct for violations of the standard assumptions. Students will also be able to generate numerical illustrations of some theoretical results
4. learn special features of modelling time series and cross sectional data.

Contribution to graduate profile

This subject will provide students with a deeper understanding of basic econometric techniques, and some of the special features of time series and cross-sectional modelling. In many quantitative professions the practical imperative is to create a single equation describing a process generated over a cross section, or over time. As a result, this course will focus mainly on single-equation time-series or cross-sectional models (and panel data models, a combination of both).

Teaching and learning strategies

Learning occurs in the context of a 2-hour lecture plus a 1-hour tutorial each week. Material is covered using several different approaches (i.e. by intuition, graphs, algebra and matrices) because not every approach works for every person.

Content

• simple regression
• multiple regression
• computing tutorial
• interpreting regression equations, and inference
• problems 1: misspecification, autocorrelation and heteroskedasticity
• problems 2: multicollinearity, autoregression
• time series 1
• time series 2: cointegration, error correction models
• time series 3: VARS, ARCH, GARCH
• errors in variables, simultaneity
• panel regression
• probit/logit
• testing efficiency (example of the foreign exchange market).

Assessment

Assessment item 1: In-Class Exams

Required text(s)

R. Carter Hill, William E. Griffiths and George G. Judge, Undergraduate Econometrics, Wiley, 2nd Edition, 2000. (HGJ)

Indicative references

Mark A. Reiman and R. Carter Hill, Using EViews for Undergraduate Econometrics, Wiley, 2nd Edition, 2000

Quantitative Micro Software, EViews 6 User's Guide, 2007

James H. Stock and Mark W. Watson, Introduction to Econometrics, Pearson, 2nd Edition, 2007

William Greene, Econometric Analysis, 6th Edition, Prentice Hall, 2008

David F. Hendry and Bent Nielsen (2007), Econometric modeling: A likelihood approach, Princeton University Press, 2007

Jeffrey M. Wooldridge, Introductory Econometrics, 3rd Edition, Thomson South-Western. 2006.

Lecture overheads

Lecture materials, computer notes, and exercises will be posted to UTS-Online each week. Note that the subject slides are not a substitute for the recommended textbook.

Other references

Faculty of Business, Guide to Writing Assignments, 2006.