Book contents
- Frontmatter
- Contents
- List of figures
- List of tables
- Preface
- 1 Introduction and outline of the book
- 2 Features of marketing research data
- 3 A continuous dependent variable
- 4 A binomial dependent variable
- 5 An unordered multinomial dependent variable
- 6 An ordered multinomial dependent variable
- 7 A limited dependent variable
- 8 A duration dependent variable
- Appendix
- Bibliography
- Author index
- Subject index
4 - A binomial dependent variable
Published online by Cambridge University Press: 06 July 2010
- Frontmatter
- Contents
- List of figures
- List of tables
- Preface
- 1 Introduction and outline of the book
- 2 Features of marketing research data
- 3 A continuous dependent variable
- 4 A binomial dependent variable
- 5 An unordered multinomial dependent variable
- 6 An ordered multinomial dependent variable
- 7 A limited dependent variable
- 8 A duration dependent variable
- Appendix
- Bibliography
- Author index
- Subject index
Summary
In this chapter we focus on the Logit model and the Probit model for binary choice, yielding a binomial dependent variable. In section 4.1 we discuss the model representations and ways to arrive at these specifications. We show that parameter interpretation is not straightforward because the parameters enter the model in a nonlinear way. We give alternative approaches to interpreting the parameters and hence the models. In section 4.2 we discuss ML estimation in substantial detail. In section 4.3, diagnostic measures, model selection and forecasting are considered. Model selection concerns the choice of regressors and the comparison of non-nested models. Forecasting deals with within-sample or out-of-sample prediction. In section 4.4 we illustrate the models for a data set on the choice between two brands of tomato ketchup. Finally, in section 4.5 we discuss issues such as unobserved heterogeneity, dynamics and sample selection.
Representation and interpretation
In chapter 3 we discussed the standard Linear Regression model, where a continuously measured variable such as sales was correlated with, for example, price and promotion variables. These promotion variables typically appear as 0/1 dummy explanatory variables in regression models. As long as such dummy variables are on the right-hand side of the regression model, standard modeling and estimation techniques can be used. However, when 0/1 dummy variables appear on the left-hand side, the analysis changes and alternative models and inference methods need to be considered. In this chapter the focus is on models for dependent variables that concern such binomial data.
- Type
- Chapter
- Information
- Quantitative Models in Marketing Research , pp. 49 - 75Publisher: Cambridge University PressPrint publication year: 2001