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Consistent estimation of zero-inflated count models.

Kevin E Staub1, Rainer Winkelmann

  • 1University of Zurich, Zürich, Switzerland.

Health Economics
|May 25, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces Poisson quasi-likelihood estimators as a robust alternative for analyzing health economics data with excess zeros. This method provides consistent estimates even when the full data distribution is unknown.

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Area of Science:

  • Health Economics
  • Biostatistics
  • Econometrics

Background:

  • Zero-inflated count data models are widely used in health economics.
  • Standard maximum likelihood estimators (MLEs) for zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) models are sensitive to distributional misspecification.
  • This sensitivity can lead to unreliable results in health economic analyses.

Purpose of the Study:

  • To propose Poisson quasi-likelihood (PQL) estimators as a robust alternative to traditional MLEs for zero-inflated count data in health economics.
  • To demonstrate that PQL estimators are consistent in the presence of excess zeros, irrespective of the true underlying distribution.
  • To illustrate the practical advantages of the PQL approach through simulations and a real-world application.

Main Methods:

  • Development and application of Poisson quasi-likelihood (PQL) estimation.
  • Monte Carlo simulations to compare the performance of PQL estimators against traditional misspecified models.
  • Empirical analysis of demand for health services using the proposed PQL method.

Main Results:

  • Poisson quasi-likelihood estimators demonstrate robustness to distributional misspecification in the presence of excess zeros.
  • Simulations confirm the consistency and reliability of PQL estimators under various data-generating processes.
  • The application to health service demand highlights the practical utility and stability of the PQL approach.

Conclusions:

  • Poisson quasi-likelihood offers a reliable and flexible method for analyzing zero-inflated count data in health economics.
  • The PQL approach mitigates the risks associated with model misspecification, leading to more trustworthy findings.
  • This methodology enhances the analysis of health service utilization and other count outcomes with excess zeros.