The generalized method of moments (GMM) estimation technique is discussed for count data models with endogenous regressors. Count data models can be specified with additive or multiplicative errors and it is shown that, in general, a set of instruments is not orthogonal to both error types....

Moment conditions are derived for dynamic linear panel data models with linear individual specific effects in the mean and multiplicative individual effects in the conditional ARCH type variance function. The relation and correlation between the linear and multiplicative effects are...

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

Monte Carlo studies have shown that estimated asymptotic standard errors of the efficient two-step generalised method of moments (GMM) estimator can be severely downward biased in small samples. The weight matrix used in the calculation of the efficient two-step GMM estimator is based on initial...

This paper presents a life cycle model for the demand for health, and derives empirical specifications that distinguish between permanent and transitory wage responses. Using panel data, we estimate dynamic health and health input demand equations. We find evidence of negative transitory wage...

For small group sizes, the Generalised Least Squares (CLS) estimator in multilevel models is biased and inconsistent when the random cluster effects are correlated with the regressors. A fixed effects approach, conditioning on the cluster effects, provides consistent estimates for the slope...

The demand for certain types of health care services depends on decisions of both the individual and the health care provider. This paper studies the conditions under which it is possible to separately identify the parameters driving the two decision processes using only count data on the total...

The benefits of panel data are well-documented but missing data problems are often more severe. In particular, units that respond in the first wave may drop out of the panel after one or more periods of participation. This paper focuses on identification and Maximum Likelihood estimation of...

In panel surveys, some observation units drop out before the end of the observation period. This panel attrition should not be ignored if it is related to the variables of interest. Hirano, Imbens, Ridder and Rubin propose the Additively Nonignorable (AN) attrition model to correct for the...