What Belongs Where? Variable Selection for Zero-Inflated Count Models with an Application to the Demand for Health Care

This paper develops stochastic search variable selection (SSVS) for zero-inflated count models which are commonly used in health economics. This allows for either model averaging or model selection in situations with many potential regressors. The proposed techniques are applied to a data set from Germany considering the demand for health care. A package for the free statistical software environment R is provided.

Longitudinal analysis of income-related health inequality: welfare foundations and alternative measures

This paper elaborates the approach to the longitudinal analysis of income-related health inequalities first proposed in Allanson, Gerdtham and Petrie (2010). In particular, the paper establishes the normative basis of their mobility indices by embedding their decomposition of the change in the health concentration index within a broader analysis of the change in “health achievement” or wellbeing. The paper further shows that their decomposition procedure can also be used to analyse the change in a range of other commonly-used incomerelated health inequality measures, including the generalised concentration index and the relative inequality index. We illustrate our work by extending their investigation of mobility in the General Health Questionnaire measure of psychological well-being over the first nine waves of the British Household Panel Survey from 1991 to 1999.

Large Time-Varying Parameter VARs

In this paper we develop methods for estimation and forecasting in large timevarying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints with likelihood-based estimation of large systems, we rely on Kalman filter estimation with forgetting factors. We also draw on ideas from the dynamic model averaging literature and extend the TVP-VAR so that its dimension can change over time. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output, and interest rates demonstrates the feasibility and usefulness of our approach.