• FNCE3002
  • Financial Econometrics

  • Credits (ECTS): 10
  • Accounting and Finance

Modules are delivered
as part of a programme.
To apply for the
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Module Description

This module introduces students to how econometric techniques can be applied in the financial field. This module follows on from Quantitative Analysis in Year 1 and Financial Mathematics in Year 2. In summary, management science techniques and statistical methods are applied to Finance and Economics.

Indicative Syllabus

Introduction to Econometrics:
The history of econometrics, the theory of and aims of econometrics, data types, the population mean and its properties.
Hypothesis Testing:
Hypothesis specification, null and alternative hypothesis, the decision rule, the T-test, type I and type II errors, confidence intervals, the F-test.
Regression Analysis:
Simple and multiple regressions, properties of the error term, regression estimates, regression methods, the residual and fitted values, OLS, R2 and adjusted R2, reverse regressions, the classical model.
Model Specification and the Associated Problems:
Choosing the independent variables, omitted variables, irrelevant variables, lagged variables, the Ramsey error specification test, functional form, dummy variables, other specification issues.
Perfect and imperfect multicollinearity, dominant variables, consequences of multicollinearity, detection of multicollinearity and variance inflation factors, remedies for multicollinearity.
Serial Correlation:
Pure and impure serial correlation, first-order and higher order serial correlation, consequences of serial correlation, detection of serial correlation and the Durbin-Watson test, correcting serial correlation.
Pure and impure heteroskedasticity, proportionality factors, consequences of heteroskedasticity, testing for heteroskedasticity using the park test or white test, remedies for heteroskedasticity.
Volatility Modelling Using ARCH/GARCH Models:
The ARCH and GARCH Family of models, testing for ARCH and GARCH effects, estimation issues, multivariate GARCH.
Discrete Choice Models:
Models for binary choice, Logit models for multiple choice, models for ordered data.
Applying Econometric Techniques:
Using statistical software (Stata) to produce econometric output, working with panel data, cross-sectional data and time-series data, testing models.

Total Contact Teaching Hours:72

Pre-requisite Modules

Title Code
Mathematics For Finance & Econ FNCE2004

Please note that the catalogue is provided as a guide to modules in DIT. Not all modules listed will necessarily be offered every year and new modules may also be added. Information subject to change. For detail on specific programmes/modules please contact the relevant School directly.