Identifying finite mixture models in the presence of moment-generating function: application in medical care using a zero-inflated binomial model

Hiroaki Masuhara ()
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Hiroaki Masuhara: Faculty of Economics and Law, Shinshu University

Economics Bulletin, 2019, vol. 39, issue 2, 1529-1537

Abstract: This study presents a simple method to identify the parameters in finite mixture models when a moment-generating function (MGF) is present. We obtain the model conditions using a zero-inflated binomial model, a simple form of the finite mixture binary model, and analyze the results using the Monte Carlo simulation. Using the zero-inflated and standard binomial models, we compare the marginal effects of health care usage.

Keywords: Finite mixtures; identifiability; zero-inflated (search for similar items in EconPapers)
JEL-codes: C1 C4 (search for similar items in EconPapers)
Date: 2019-06-15
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