Book contents
- Frontmatter
- Contents
- List of figures
- List of tables
- List of boxes
- List of screenshots
- Preface to the second edition
- Acknowledgements
- 1 Introduction
- 2 A brief overview of the classical linear regression model
- 3 Further development and analysis of the classical linear regression model
- 4 Classical linear regression model assumptions and diagnostic tests
- 5 Univariate time series modelling and forecasting
- 6 Multivariate models
- 7 Modelling long-run relationships in finance
- 8 Modelling volatility and correlation
- 9 Switching models
- 10 Panel data
- 11 Limited dependent variable models
- 12 Simulation methods
- 13 Conducting empirical research or doing a project or dissertation in finance
- 14 Recent and future developments in the modelling of financial time series
- Appendix 1 A review of some fundamental mathematical and statistical concepts
- Appendix 2 Tables of statistical distributions
- Appendix 3 Sources of data used in this book
- References
- Index
11 - Limited dependent variable models
- Frontmatter
- Contents
- List of figures
- List of tables
- List of boxes
- List of screenshots
- Preface to the second edition
- Acknowledgements
- 1 Introduction
- 2 A brief overview of the classical linear regression model
- 3 Further development and analysis of the classical linear regression model
- 4 Classical linear regression model assumptions and diagnostic tests
- 5 Univariate time series modelling and forecasting
- 6 Multivariate models
- 7 Modelling long-run relationships in finance
- 8 Modelling volatility and correlation
- 9 Switching models
- 10 Panel data
- 11 Limited dependent variable models
- 12 Simulation methods
- 13 Conducting empirical research or doing a project or dissertation in finance
- 14 Recent and future developments in the modelling of financial time series
- Appendix 1 A review of some fundamental mathematical and statistical concepts
- Appendix 2 Tables of statistical distributions
- Appendix 3 Sources of data used in this book
- References
- Index
Summary
Learning Outcomes
In this chapter, you will learn how to
Compare between different types of limited dependent variables and select the appropriate model
Interpret and evaluate logit and probit models
Distinguish between the binomial and multinomial cases
Deal appropriately with censored and truncated dependent variables
Estimate limited dependent variable models using maximum likelihood in EViews
Introduction and motivation
Chapters 4 and 9 have shown various uses of dummy variables to numerically capture the information qualitative variables – for example, day-of-the-week effects, gender, credit ratings, etc. When a dummy is used as an explanatory variable in a regression model, this usually does not give rise to any particular problems (so long as one is careful to avoid the dummy variable trap – see chapter 9). However, there are many situations in financial research where it is the explained variable, rather than one or more of the explanatory variables, that is qualitative. The qualitative information would then be coded as a dummy variable and the situation would be referred to as a limited dependent variable and needs to be treated differently. The term refers to any problem where the values that the dependent variables may take are limited to certain integers (e.g. 0, 1, 2, 3, 4) or even where it is a binary number (only 0 or 1).
- Type
- Chapter
- Information
- Introductory Econometrics for Finance , pp. 511 - 545Publisher: Cambridge University PressPrint publication year: 2008