Using count data on the number of bank failures in US states during the 1960 to 2006 period, this paper endeavors to establish how far sources of economic risk (recessions, high interest rates, inflation) or differences in solvency and branching regulation can explain some of the fragility in...

We propose a dynamic factor model for the analysis of multivariate time series count data. Our model allows for idiosyncratic as well as common serially correlated latent factors in order to account for potentially complex dynamic interdependence between series of counts. The model is estimated...

This work aims to identify the determinants of health care utilization in Portugal and to quantify their effects. A clear idea about this may help the policy debate on the delivery and organization of health services. Toward this end, we developed an approach based on count data models. We found...

In a high-dimensional linear regression model, we propose a new procedure for testing statistical significance of a subset of regression coefficients. Specifically, we employ the partial covariances between the response variable and the tested covariates to obtain a test statistic. The resulting...

To alleviate the computational burden of making the relevant estimation algorithms stable for nonlinear and semiparametric regression models with, particularly, high-dimensional data, a transformation-based method combining sufficient dimension reduction approach is proposed. To this end,...

The paper proposes a combination of finite mixture models and matching estimators to account for heterogeneous and nonlinear effects of the coinsurance rate on healthcare expenditure. We use loglinear model and generalized linear models with different distribution families, and measure the...