We study job durations using a multivariate hazard model allowing for worker-specific and firm-specific unobserved determinants. The latter are captured by unobserved heterogeneity terms or random effects, one at the firm level and another at the worker level. This enables us to decompose the...

We study job durations using a multivariate hazard model allowing for worker-specific and firm-specific unobserved determinants. The latter are captured by unobserved heterogeneity terms or random effects, one at the firm level and another at the worker level. This enables us to decompose the...

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...

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...

This paper extends a stochastic conditional duration (SCD) model for financial transaction data to allow for correlation between error processes or innovations of observed duration process and latent log duration process with the aim of improving the statistical fit of the model. Suitable...

In this paper, we develop and apply Bayesian inference for an extended Nelson-Siegel (1987) term structure model capturing interest rate risk. The so-called Stochastic Volatility Nelson-Siegel (SVNS) model allows for stochastic volatility in the underlying yield factors. We propose a Markov...

This note presents the R package bayesGARCH (Ardia, 2007) which provides functions for the Bayesian estimation of the parsimonious and effective GARCH(1,1) model with Student-t innovations. The estimation procedure is fully automatic and thus avoids the tedious task of tuning a MCMC sampling...

We propose a generic Markov Chain Monte Carlo (MCMC) algorithm to speed up computations for datasets with many observations. A key feature of our approach is the use of the highly efficient difference estimator from the survey sampling literature to estimate the log-likelihood accurately using...

Following Lancaster (2002), we propose a strategy to solve the incidental parameter problem. The method is demonstrated under a simple panel Poisson count model. We also extend the strategy to accomodate cases when information orthogonality is unavailable, such as the linear AR(p) panel model....