Multiple Categorical Covariates-Based Multinomial Dynamic Response Model

R. Prabhakar Rao and Brajendra C. Sutradhar ()
Additional contact information
R. Prabhakar Rao: Sri Sathya Sai Institute of Higher Learning
Brajendra C. Sutradhar: Carleton University

Sankhya A: The Indian Journal of Statistics, 2020, vol. 82, issue 1, No 9, 186-219

Abstract: Abstract Regression models for multinomial responses with time dependent covariates have been studied recently both in longitudinal and time series setup. For practical importance, in this paper, we focus on a longitudinal multinomial response model with two categorical covariates to study their main and interaction effects after accommodating a lag 1 dynamic relationship between past and present multinomial responses. The proposed model could be generalized easily to accommodate multiple (more than two) categorical covariates and their interactions. As far as the estimation of the regression and the dynamic dependence parameters is concerned, we follow a recent parameter dimension-split based approach suggested by Sutradhar (Sankhya A80, 301–329 2018) but unlike the conditional method of moments (CMM) used in this study, we use a more efficient estimation approach, namely the so-called conditional generalized quasi-likelihood (CGQL) method for the estimation of the dynamic dependence parameters. The regression parameters are also estimated by using the same CGQL approach where responses become independent conditional on the past responses which is similar in principle to the likelihood estimation where the likelihood function is formed as a product of transitional probabilities conditional on the past responses. The asymptotic properties of the CGQL estimators are provided in details. The higher efficiency performance of the CGQL approach over the CMM approach is also demonstrated, for example, for the estimation of the dynamic dependence parameters.

Keywords: Asymptotic properties; Covariates with possible interactions; Dynamic dependence parameters; Conditional generalized quasi-likelihood estimation; Lag 1 transitional multinomial probabilities; Unconditional method of moments; Primary 62H12; 62F12; Secondary 62F10 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13171-019-00168-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sankha:v:82:y:2020:i:1:d:10.1007_s13171-019-00168-1

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/13171

DOI: 10.1007/s13171-019-00168-1

Access Statistics for this article

Sankhya A: The Indian Journal of Statistics is currently edited by Dipak Dey

More articles in Sankhya A: The Indian Journal of Statistics from Springer, Indian Statistical Institute
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().