An Econometric Model of Health Care Demand with Non-Linear Pricing

Kunz, Johannes; Winkelmann, Rainer

doi:10.2139/ssrn.2658235

Cited by 2 publications

(1 citation statement)

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“…Cameron & Trivedi, 1986; Winkelmann, 2004). Specifically, we zoom in on the extensive margin decision, and correspondingly on the first step of a possible hurdle count data model (Mullahy, 1986; Pohlmeier & Ulrich, 1995, Kunz & Winkelmann, 2017). Our approach also relates more broadly to other strands of the health economics literature.…”

Section: Application To the Determinants Of Doctor Visitsmentioning

confidence: 99%

Predicting Individual Effects in Fixed Effects Panel Probit Models

Kunz

Staub

Winkelmann

2021

Journal of the Royal Statistical Society Series A: Statistics in Society

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Many applied settings in empirical economics require estimation of a large number of individual effects, like teacher effects or location effects; in health economics, prominent examples include patient effects, doctor effects or hospital effects. Increasingly, these effects are the object of interest of the estimation, and predicted effects are often used for further descriptive and regression analyses. To avoid imposing distributional assumptions on these effects, they are typically estimated via fixed effects methods. In short panels, the conventional maximum likelihood estimator for fixed effects binary response models provides poor estimates of these individual effects since the finite sample bias is typically substantial. We present a bias-reduced fixed effects estimator that provides better estimates of the individual effects in these models by removing the first-order asymptotic bias. An additional, practical advantage of the estimator is that it provides finite predictions for all individual effects in the sample, including those for which the corresponding dependent variable has identical outcomes in all time periods over time (either all zeros or ones); for these, the maximum likelihood prediction is infinite. We illustrate the approach in simulation experiments and in an application to health care utilization.

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