Introductory econometrics for finance / Chris Brooks
Material type:
TextLanguage: English Publisher: Cambridge, United Kingdom, Cambridge University Press, 2019Edition: Fourth editionDescription: xxxi, 696 pages ; 25 cmISBN: 9781108422536; 9781108436823Subject(s): Finance -- Econometric models | EconometricsDDC classification: 332.015 | Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| Books | Institute of Public Enterprise, Library S Campus | 332.015 BRO (Browse shelf) | Available | 48388 |
Includes bibliographical references (pages 672-687) and index.
Introduction and mathematical foundations
Statistical foundations and dealing with data
A brief overview of the classical linear regression model
Further development and analysis of the classical linear regression model
Classical linear regression model assumptions and diagnostic tests
Univariate time-series modelling and forecasting
Multivariate models
Modelling long-run relationships in finance – Modelling volatility and correlation
Switching and state space models
Panel data
Limited dependent variable models
Simulation methods
Additional econometric techniques for financial research
Conducting empirical research or doing a project or dissertation in finance
Description Contents Resources Courses About the AuthorsA complete resource for finance students, this textbook presents the most common empirical approaches in finance in a comprehensive and well-illustrated manner that shows how econometrics is used in practice, and includes detailed case studies to explain how the techniques are used in relevant financial contexts. Maintaining the accessible prose and clear examples of previous editions, the new edition of this best-selling textbook provides support for the main industry-standard software packages, expands the coverage of introductory mathematical and statistical techniques into two chapters for students without prior econometrics knowledge, and includes a new chapter on advanced methods. Learning outcomes, key concepts and end-of-chapter review questions (with full solutions online) highlight the main chapter takeaways and allow students to self-assess their understanding. Online resources include extensive teacher and student support materials, including EViews, Stata, R, and Python software guides
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