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Author: Horowitz, Joel L.
Resulting in 3 citations.
| 1. |
Horowitz, Joel L. Lee, Sokbae |
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Semiparametric Estimation of a Panel Data Proportional Hazards Model with Fixed Effects cemmap Working Papers CWP21/02, Institute for Fiscal Studies: London, UK, 2002. Also: http://www.cemmap.ac.uk/publications.php?publication_id=2649 Cohort(s): NLSY79 Publisher: Institute for Fiscal Studies (IFS), London Keyword(s): Data Analysis; Job Turnover; Modeling, Fixed Effects; Modeling, Hazard/Event History/Survival/Duration; Monte Carlo; Statistical Analysis; Work History Permission to reprint the abstract has not been received from the publisher. This paper considers a panel duration model that has a proportional hazards specification with fixed effects. The paper shows how to estimate the baseline and integrated baseline hazard functions without assuming that they belong to known, finite-dimensional families of functions. Existing estimators assume that the baseline hazard function belongs to a known parametric family. Therefore, the estimators presented here are more general than existing ones. This paper also presents a method for estimating the parametric part of the proportional hazards model under dependent right censoring, under which the partial likelihood estimator is inconsistent. The paper presents some Monte Carlo evidence on the small sample performance of the new estimators. Finally, the estimation methods are illustrated by applying them to National Longitudinal Survey of Youth work history data. The estimated, inverted U-shaped baseline hazard function of job ending suggests that the data are consistent with the job matching theory of Jovanovic (1979). |
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Bibliography Citation
Horowitz, Joel L. and Sokbae Lee. "Semiparametric Estimation of a Panel Data Proportional Hazards Model with Fixed Effects." cemmap Working Papers CWP21/02, Institute for Fiscal Studies: London, UK, 2002. |
| 2. |
Horowitz, Joel L. Lee, Sokbae |
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Semiparametric Estimation of a Panel Data Proportional Hazards Model with Fixed Effects Journal of Econometrics 119,1 (March 2004): 155-198. Also: http://www.sciencedirect.com/science/article/pii/S0304407603002033 Cohort(s): NLSY79 Publisher: Elsevier Keyword(s): Modeling, Fixed Effects; Modeling, Hazard/Event History/Survival/Duration; Monte Carlo; Statistical Analysis; Work History This paper considers a panel duration model that has a proportional hazards specification with fixed effects. The paper shows how to estimate the baseline and integrated baseline hazard functions without assuming that they belong to known, finite-dimensional families of functions. Existing estimators assume that the baseline hazard function belongs to a known parametric family. Therefore, the estimators presented here are more general than existing ones. This paper also presents a method for estimating the parametric part of the proportional hazards model with dependent right censoring, under which the partial likelihood estimator is inconsistent. The paper presents some Monte Carlo evidence on the small sample performance of the new estimators. |
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Bibliography Citation
Horowitz, Joel L. and Sokbae Lee. "Semiparametric Estimation of a Panel Data Proportional Hazards Model with Fixed Effects." Journal of Econometrics 119,1 (March 2004): 155-198.
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| 3. |
Horowitz, Joel L. Manski, Charles F. |
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Censoring of Outcomes and Regressors Due to Survey Nonresponse: Identification and Estimation Using Weights and Imputations Journal of Econometrics 84,1 (May 1998): 37-58. Also: http://www.sciencedirect.com/science/article/pii/S0304407697000778 Cohort(s): NLSY79 Publisher: Elsevier Keyword(s): Data Quality/Consistency; Longitudinal Data Sets; Longitudinal Surveys; Nonresponse Survey nonresponse makes identification of population parameters problematic. Except in special cases, identification is possible only if one makes untestable assumptions about the distribution of the missing data. However, nonresponse does not preclude identification of bounds on parameters. This paper shows how identified bounds on unidentified population parameters can be obtained under several forms of nonresponse. Organizations conducting major surveys commonly release public-use data files that provide nonresponse weights or imputations to be used for estimating population parameters. The paper shows how to bound the asymptotic bias of estimates using weights and imputations. The results are illustrated with empirical examples based on the National Longitudinal Survey of Youth. Photocopy available from ABI/INFORM. |
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Bibliography Citation
Horowitz, Joel L. and Charles F. Manski. "Censoring of Outcomes and Regressors Due to Survey Nonresponse: Identification and Estimation Using Weights and Imputations." Journal of Econometrics 84,1 (May 1998): 37-58.
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