Event Dependence and Heterogeneity in Duration Models: The Conditional Frailty Model

2007 ◽  
Vol 15 (3) ◽  
pp. 237-256 ◽  
Author(s):  
Janet M. Box-Steffensmeier ◽  
Suzanna De Boef ◽  
Kyle A. Joyce

We introduce the conditional frailty model, an event history model that separates and accounts for both event dependence and heterogeneity in repeated events processes. Event dependence and heterogeneity create within-subject correlation in event times thereby violating the assumptions of standard event history models. Simulations show the advantage of the conditional frailty model. Specifically they demonstrate the model's ability to disentangle the sources of within-subject correlation as well as the gains in both efficiency and bias of the model when compared to the widely used alternatives, which often produce conflicting conclusions. Two substantive political science problems illustrate the usefulness and interpretation of the model: state policy adoption and terrorist attacks.

2014 ◽  
Vol 22 (2) ◽  
pp. 183-204 ◽  
Author(s):  
Janet M. Box-Steffensmeier ◽  
Suzanna Linn ◽  
Corwin D. Smidt

Estimators within the Cox family are often used to estimate models for repeated events. Yet, there is much we still do not know about the performance of these estimators. In particular, we do not know how they perform given time dependence, different censoring rates, and a varying number of events and sample sizes. We use Monte Carlo simulations to demonstrate the performance of a variety of popular semi-parametric estimators as these data aspects change and under conditions of event dependence and heterogeneity, both, or neither. We conclude that the conditional frailty model outperforms other standard estimators under a wide array of data-generating processes, and data limitations rarely alter its performance.


1997 ◽  
Vol 41 (4) ◽  
pp. 1414 ◽  
Author(s):  
Janet M. Box-Steffensmeier ◽  
Bradford S. Jones

2001 ◽  
Vol 45 (4) ◽  
pp. 972 ◽  
Author(s):  
Janet M. Box-Steffensmeier ◽  
Christopher J. W. Zorn

2016 ◽  
Vol 8 (4) ◽  
pp. 534-552 ◽  
Author(s):  
Krishna P. Paudel ◽  
Nirmala Devkota ◽  
Ying Tan

Purpose The purpose of this paper is to address the issues of correlated events and individual heterogeneity in multiple best management practices (BMPs) adoption. Design/methodology/approach The authors used survey data collected from broiler producers in Louisiana, USA. The authors estimated several duration models that either considered event dependence or heterogeneity or both. Findings Results from the conditional frailty model indicated that large farms adopt BMPs earlier, farmers who have been in broiler farming profession for a long time are late to adopt BMPs and more informed farmers, through contact with extension agents and education, are early adopters of BMPs. Research limitations/implications The limitation of this study is that the authors did not validate the robustness of the conditional frailty model using a more rigorous approach, such as empirical simulation method. Practical implications Many farmers do not adopt a new technology immediately after it becomes available. Duration models help to understand why farmers wait and how long they wait before adopting a new technology. In case of correlated events, where farmers adopt more than one technology, it is important to know the driving factors behind multiple technologies adoption. The findings from this study should help to properly target farmers to increase the adoption rate of a desired BMP. Originality/value This is the first study in agriculture technology adoption literature that uses a conditional frailty model to understand why farmers wait to adopt a new technology. This study also addresses both dependence in BMP adoption and heterogeneity in farmers’ quality that impact technology adoption.


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