(Spatial) panel data are routinely modelled in discrete time (DT). However, there are compelling arguments for continuous time (CT) modelling of (spatial) panel data. Particularly, most social processes evolve in CT, so that statistical analysis in DT is an oversimplification, gives an...

We present new results for the likelihood-based analysis of the dynamic factor model that possibly includes intercepts and explanatory variables. The latent factors are modelled by stochastic processes. The idiosyncratic disturbances are specified as autoregressive processes with mutually...

We apply a recently proposed method to disentangle unobserved heterogeneity from risk in returns to education. We replicate the original study on US men and extend to US women, UK men and German men. Most original results are not robust. A college education cannot universally be considered an...

We investigate the presence of moral hazard and advantageous or adverse selection in a market for supplementary health insurance. For this we specify and estimate dynamic models for health insurance decisions and health care utilization. Estimates of the health care utilization models indicate...

The literature that tests for U-shaped relationships using panel data, such as those between pollution and income or inequality and growth, reports widely divergent (parametric and non-parametric) empirical findings. We explain why lack of identification lies at the root of these differences. To...

This paper uses micro data from four OECD countries (the United States, Spain, Italy, and the Netherlands), to assess the determinants of household debt holding and to investigate whether or not credit constraints are important for household debt holding. We extend the existing literature in...

We study risk behavior of Danish self-employed entrepreneurs, whose income risk may be driven by both exogenous factors and effort choice (moral hazard). Partial insurance is available through voluntary unemployment insurance (UI). Additional incentives to sign insurance contracts stem from a...

We develop a new simultaneous time series model for volatility and dependence with long memory (fractionally integrated) dynamics and heavy-tailed densities. Our new multivariate model accounts for typical empirical features in financial time series while being robust to outliers or jumps in the...

We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts generalized autoregressive score dynamics to obtain a time-varying covariance matrix of the multivariate...

Market share models for weekly store-level data are useful to understand competitive structures by delivering own and cross price elasticities. These models can however not be used to examine which brands lose share to which brands during a specific period of time. It is for this purpose that we...