Faster estimation of discrete time duration models with unobserved heterogeneity using hshaz2
This article presents hshaz2, a new Stata command that uses d2 ml method to estimate discrete time duration models with unobserved heterogeneity. The main advantage of using hshaz2 is the gain in computation speed, that takes special relevance as the sample size increases. Estimation results show th...
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| Tipo de recurso: | informe técnico |
| Estado: | Versión publicada |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/134453 |
| Acceso en línea: | https://hdl.handle.net/11441/134453 |
| Access Level: | acceso abierto |
| Palabra clave: | Duration analysis Unobserved heterogeneity d2 ml method hshaz hshaz2 Hessian matrix |
| Sumario: | This article presents hshaz2, a new Stata command that uses d2 ml method to estimate discrete time duration models with unobserved heterogeneity. The main advantage of using hshaz2 is the gain in computation speed, that takes special relevance as the sample size increases. Estimation results show that, on a sample size of 568,042 observations, hshaz2 spends 0.42 and 1.13 minutes to achieve the convergence of a discrete time proportional hazard model with two and three points of support, respectively. Furthermore, hshaz2 allows for the estimation of multispell duration models, where individuals may be observed at risk of exiting more than once. Using, a sample with 1,547,507 observations, hshaz2 spends 1.17 and 2.17 minutes to achieve convergence of a multispell discrete time proportional hazard model with two and three points of support, respectively. |
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