Professor Frank Windmeijer

In this study we look at the relationship between income and healthy behaviour over the generations by studying the association between parental income and children's prevalence to smoke in Britain using data from the British Household Panel Survey and British Youth Survey.

Monte Carlo studies have shown that estimated asymptotic standard errors of the efficient two-step generalized method of moments (GMM) estimator can be severely downward biased in small samples.

We report an empirical analysis of the responses of the supply and demand for secondary care to waiting list size and waiting times.

This Briefing Note looks at the potential for the introduction of a 'fat tax' into the UK in an effort to reduce the growing prevalence of obesity in Britain. This Briefing Note looks at trends in UK obesity and examines evidence on eating habits and exercise in order to see whether trends here can account for what we see happening to obesity. We go on to review some of the key economic reasons behind why we might be concerned about obesity and why we might consider there to be a case for government intervention. We also discuss how food is currently taxed and the various ways in which a 'fat tax' might be introduced, looking at particular issues the government might need to address should it wish to introduce one. We finish by presenting some simple analysis of a hypothetical 'fat tax' in terms of how it might impact differently on the rich and the poor.

Many health-care systems allocate funding according to measures of need. The utilisation approach for measuring need rests on the assumptions that use of health care is determined by demand and supply and that need is an important element of demand.

In this paper we explore a new approach to estimation for autoregressive panel data models, based on projecting the unobserved individual effects on the vector of observations on the lagged dependent variable.

In this paper we explore a new approach to estimation for autoregressive panel data models, based on projecting the unobserved individual effects on the vector of observations on the lagged dependent variable.

ExpEnd is a Gauss programme for non-linear generalised method of moments (GMM) estimation of exponential models with endogenous regressors for cross section and panel data.

In this paper we examine the panel data estimation of dynamic models for count data that include correlated fixed effects and predetermined variables.

Examining the panel data estimation of dynamic models for count data that include correlated fixed effects and predetermined variables.

We compare the finite sample performance of a range of tests of linear restrictions for linear panel data models estimated using Generalised Method of Moments (GMM).

In this paper we explore a new approach to estimation for autoregressive panel data models, based on projecting the unobserved individual effects on the vector of observations on the lagged dependent variable.

The demand for certain types of health care services depends on decisions of both the individual and the health care provider.

In this paper we examine the properties of a simple criterion-based, likelihood ratio type test of parameter restristions for standard GMM estimators in autoregressive panel data models.

In a Monte Carlo study of a panel data model it is shown that the corrected variance estimate approximates the final sample variance well, leading to more accurate inference.

This paper presents a life cycle model for the demand for health, and derives empirical specifications that distinguish between permanent and transitory wage responses.