Shared-frailty survival models specify that systematic unobserved determinants of duration outcomes are identical within groups of individuals. We consider random-effects likelihood-based statistical inference if the duration data are subject to left-truncation. Such inference with...

Likelihood based inference for multi-state latent factor intensity models is hindered by the fact that exact closed-form expressions for the implied data density are not available. This is a common and well-known problem for most parameter driven dynamic econometric models. This paper reviews,...

A common practice in modeling intertrade durations is to use various hazard functions for data fitting. This paper calls into question this practice and suggests mixing the hazard function as exponential. An underlying hypothesis of the suggestion is that duration data have a mixture of...

This paper builds on the Empirical Monte Carlo simulation approach developed by Huber et al. (2013) to study the estimation of Timing-of-Events (ToE) models. We exploit rich Swedish data of unemployed job-seekers with information on participation in a training program to simulate placebo...

This paper builds on the Empirical Monte Carlo simulation approach developed by Huber et al. (2013) to study the estimation of Timing-of-Events (ToE) models. We exploit rich Swedish data of unemployed job-seekers with information on participation in a training program to simulate placebo...

Consider a setting where a treatment that starts at some point during a spell (e.g. in unemployment) may impact on the hazard rate of the spell duration, and where the impact may be heterogeneous across subjects. We provide Monte Carlo evidence on the feasibility of estimating the distribution...

This paper proposes a variant of a threshold stochastic conditional duration (TSCD) model for financial data at the transaction level. It assumes that the innovations of the duration process follow a threshold distribution with a positive support. In addition, it also assumes that the latent...