1995
DOI: 10.1111/j.1468-0084.1995.tb00031.x
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Easy Estimation Methods for Discrete‐Time Duration Models

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Cited by 671 publications
(537 citation statements)
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“…In addition, these models can be estimated using conventional regression techniques for binary response panel data (see e.g. Jenkins, 1995). 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.…”
Section: Methodsmentioning
confidence: 99%
“…In addition, these models can be estimated using conventional regression techniques for binary response panel data (see e.g. Jenkins, 1995). 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.…”
Section: Methodsmentioning
confidence: 99%
“…As mentioned in the Introduction, we test the former by demonstrating that the duration between the end of compulsory education at the age of 18 and each year of potential graduation is unrelated to the provincial unemployment rate in those years. 18 In a nutshell, we estimate a proportional discrete hazard model (Kiefer, 1988;Jenkins, 1995) that regresses a yearly indicator of graduation since age 17 on observed individual characteristics and the province of living measured at age 17, the elapsed duration (in years) in education since age 17, and the provincial unemployment rate in each potential year of graduation (i.e. an interaction term with the elapsed duration).…”
Section: Estimation Strategymentioning
confidence: 99%
“…Part of such variation can be accounted for by controlling for household's observed individual characteristics. Accordingly, we specify a complementary log-log hazard rate (Jenkins, 1995): h(j, X) = 1 -exp[-exp('X+ j )], where , the baseline hazard, is modeled as a piecewise-constant function by using dummy variables for each year in poverty. The matrix of covariates X contains the household, farm and village characteristics and β is a vector of parameters to be estimated.…”
Section: Poverty Exitsmentioning
confidence: 99%