1995
DOI: 10.1002/jae.3950100406
|View full text |Cite
|
Sign up to set email alerts
|

A class of binary response models for grouped duration data

Abstract: This paper explores the relationship between conventional models for binary response such as the probit and logit, and the proportional hazard (PH) and related specifications for grouped duration data. I outline a general class of hazard models for grouped duration data based upon the choice of period‐specific distribution functions, facilitating a thorough analysis of the implications of various specifications and consideration of various issues of model identification. This class of models nests, among other… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

2
91
0
5

Year Published

1997
1997
2023
2023

Publication Types

Select...
10

Relationship

0
10

Authors

Journals

citations
Cited by 163 publications
(98 citation statements)
references
References 13 publications
2
91
0
5
Order By: Relevance
“…While it can be shown that a stacked binary choice model employing a complementary log-log (cloglog) link function represents the exact groupedduration analogue of the Cox proportional hazards model, the more familiar logit and probit specifications do not imply the proportional hazards assumption (see e.g. Sueyoshi, 1995). Each of these models shares the virtues of the Cox model to allow for right censoring and a nonparametric estimation of the baseline hazard function.…”
Section: Methodsmentioning
confidence: 99%
“…While it can be shown that a stacked binary choice model employing a complementary log-log (cloglog) link function represents the exact groupedduration analogue of the Cox proportional hazards model, the more familiar logit and probit specifications do not imply the proportional hazards assumption (see e.g. Sueyoshi, 1995). Each of these models shares the virtues of the Cox model to allow for right censoring and a nonparametric estimation of the baseline hazard function.…”
Section: Methodsmentioning
confidence: 99%
“…If the discrete hazard rate P(t) is the conditional probability that a household i invests in energy efficiency in a particular year t, given that it has not adopted any energy efficiency measures before, straightforward logistic regression can be applied and the survival model can be specified as follows (Allison, 2014;Sueyoshi, 1995):…”
Section: Methodsmentioning
confidence: 99%
“…Therefore, we include twelve dummy variables indicating the duration of the current spell in paid employment in the vector xit. By conditioning on this very flexible specification of the baseline hazard, the probit model of the transition probability in equation (2), estimated on the data in person-period format, can equivalently be written as a general survival model, as described in Jenkins (1995), Sueyoshi (1995), and Caliendo et al (2010). We use annual data because the survey interviews occur once a year and the covariates are not available at higher frequencies.…”
Section: Statutory and Private Health Insurancementioning
confidence: 99%