Book contents
- Frontmatter
- Contents
- List of tables
- List of figures
- Preface
- 1 Introduction: random utility and ordered choice models
- 2 Modeling binary choices
- 3 A model for ordered choices
- 4 Antecedents and contemporary counterparts
- 5 Estimation, inference and analysis using the ordered choice model
- 6 Specification issues and generalized models
- 7 Accommodating individual heterogeneity
- 8 Parameter variation and a generalized model
- 9 Ordered choice modeling with panel and time series data
- 10 Bivariate and multivariate ordered choice models
- 11 Two-part and sample selection models
- 12 Semiparametric and nonparametric estimators and analyses
- References
- Index
5 - Estimation, inference and analysis using the ordered choice model
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- List of tables
- List of figures
- Preface
- 1 Introduction: random utility and ordered choice models
- 2 Modeling binary choices
- 3 A model for ordered choices
- 4 Antecedents and contemporary counterparts
- 5 Estimation, inference and analysis using the ordered choice model
- 6 Specification issues and generalized models
- 7 Accommodating individual heterogeneity
- 8 Parameter variation and a generalized model
- 9 Ordered choice modeling with panel and time series data
- 10 Bivariate and multivariate ordered choice models
- 11 Two-part and sample selection models
- 12 Semiparametric and nonparametric estimators and analyses
- References
- Index
Summary
In this chapter, we will survey the elements of estimation, inference, and analysis with the ordered choice model. It will prove useful to develop an application as part of the discussion.
Application of the ordered choice model to self-assessed health status
Riphahn et al. (2003) analyzed individual data on health care utilization (doctor visits and hospital visits) using various models for counts. The data set is an unbalanced panel of 7,293 German households observed from one to seven times for a total of 27,326 observations, extracted from the German Socioeconomic Panel (GSOEP). (See Riphahn et al. (2003) and Greene (2008a) for discussion of the data set in detail.) Among the variables in this data set is HSAT, a self-reported health assessment that is recorded with values 0,1,…,10 (so, J = 10). Figure 5.1 shows the distribution of outcomes for the full sample: The figure reports the variable NewHSAT, not the original variable. Forty of the 27,326 observations on HSAT in the original data were coded with noninteger values between 6.5 and 6.95. We have changed these forty observations to sevens. In order to construct a compact example that is sufficiently general to illustrate the technique, we will aggregate the categories shown as follows: (0–2) = 0, (3–5) = 1, (6–8) = 2, (9) = 3, (10) = 4. (One might expect collapsing the data in this fashion to sacrifice some information and, in turn, produce a less efficient estimator of the model parameters. See Murad et al. (2003) for some analysis of this issue.)
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- Information
- Modeling Ordered ChoicesA Primer, pp. 136 - 180Publisher: Cambridge University PressPrint publication year: 2010