Book contents
- Frontmatter
- Contents
- List of figures
- List of tables
- List of boxes
- List of screenshots
- Preface to the third edition
- Acknowledgements
- 1 Introduction
- 2 Mathematical and statistical foundations
- 3 A brief overview of the classical linear regression model
- 4 Further development and analysis of the classical linear regression model
- 5 Classical linear regression model assumptions and diagnostic tests
- 6 Univariate time series modelling and forecasting
- 7 Multivariate models
- 8 Modelling long-run relationships in finance
- 9 Modelling volatility and correlation
- 10 Switching models
- 11 Panel data
- 12 Limited dependent variable models
- 13 Simulation methods
- 14 Conducting empirical research or doing a project or dissertation in finance
- Appendix 1 Sources of data used in this book
- Appendix 2 Tables of statistical distributions
- Glossary
- References
- Index
3 - A brief overview of the classical linear regression model
- Frontmatter
- Contents
- List of figures
- List of tables
- List of boxes
- List of screenshots
- Preface to the third edition
- Acknowledgements
- 1 Introduction
- 2 Mathematical and statistical foundations
- 3 A brief overview of the classical linear regression model
- 4 Further development and analysis of the classical linear regression model
- 5 Classical linear regression model assumptions and diagnostic tests
- 6 Univariate time series modelling and forecasting
- 7 Multivariate models
- 8 Modelling long-run relationships in finance
- 9 Modelling volatility and correlation
- 10 Switching models
- 11 Panel data
- 12 Limited dependent variable models
- 13 Simulation methods
- 14 Conducting empirical research or doing a project or dissertation in finance
- Appendix 1 Sources of data used in this book
- Appendix 2 Tables of statistical distributions
- Glossary
- References
- Index
Summary
Learning outcomes
In this chapter, you will learn how to
• Derive the OLS formulae for estimating parameters and their standard errors
• Explain the desirable properties that a good estimator should have
• Discuss the factors that affect the sizes of standard errors
• Test hypotheses using the test of significance and confidence interval approaches
• Interpret p-values
• Estimate regression models and test single hypotheses in EViews
What is a regression model?
Regression analysis is almost certainly the most important tool at the econometrician's disposal. But what is regression analysis? In very general terms, regression is concerned with describing and evaluating the relationship between a given variable and one or more other variables. More specifically, regression is an attempt to explain movements in a variable by reference to movements in one or more other variables.
To make this more concrete, denote the variable whose movements the regression seeks to explain by y and the variables which are used to explain those variations by x1, x2, …, xk. Hence, in this relatively simple setup, it would be said that variations in k variables (the xs) cause changes in some other variable, y. This chapter will be limited to the case where the model seeks to explain changes in only one variable y (although this restriction will be removed in chapter 7).
There are various completely interchangeable names for y and the xs, and all of these terms will be used synonymously in this book (see box 3.1).
- Type
- Chapter
- Information
- Introductory Econometrics for Finance , pp. 75 - 133Publisher: Cambridge University PressPrint publication year: 2014
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