Semi-Parametric Models for Negative Binomial Panel Data

Brajendra C. Sutradhar (), Vandna Jowaheer and R. Prabhakar Rao
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Brajendra C. Sutradhar: Memorial University
Vandna Jowaheer: University of Mauritius
R. Prabhakar Rao: Sri Sathya Sai Institute of Higher Learning

Sankhya A: The Indian Journal of Statistics, 2016, vol. 78, issue 2, No 7, 269-303

Abstract: Abstract This paper considers a semi-parametric model for longitudinal negative binomial counts under the assumption that the repeated count responses follow an ARMA type non-stationary correlation structure. A step-by-step estimation approach is developed which provides consistent estimators for the non-parametric function, the auto-correlation structure and overdispersion parameter involved in the marginal negative binomial model, subsequently yielding a consistent estimator for the main regression parameter. Proofs for the consistency properties of the estimators are given. Also the convergence rates for the estimators of the non-parametric function as well as main parameters of the model are derived.

Keywords: Auto-correlations for negative binomial counts; Kernel based semi-parametric generalized quasi-likelihood estimation; Moments for correlation estimation; Non-parametric function; Quasi-likelihood estimation; Semi-parametric marginal regression model; Primary 62F10, 62H20; Secondary 62F12, 62H12 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13171-016-0089-8

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