Joint estimation using quadratic estimating function
type: Item
instance of: scholarly article
title: Joint estimation using quadratic estimating function
zbMATH Open document ID: 1229.62124
DOI: 10.1155/2011/372512
author: A. Thavaneswaran
author: You Liang
author: Bovas Abraham
published in: Journal of Probability and Statistics
publication date: 2011-10-26T00:00:00Z
review text: Summary: A class of martingale estimating functions is convenient and plays an important role for inference for nonlinear time series models. However, when the information about the first four conditional moments of the observed process becomes available, the quadratic estimating functions are more informative. In this paper, a general framework for joint estimation of conditional mean and variance parameters in time series models using quadratic estimating functions is developed. Superiority of the approach is demonstrated by comparing the information associated with the optimal quadratic estimating function with the information associated with other estimating functions. The method is used to study the optimal quadratic estimating functions of the parameters of autoregressive conditional duration (ACD) models, random coefficient autoregressive (RCA) models, doubly stochastic models and regression models with ARCH errors. Closed-form expressions for the information gain are also discussed in some detail.
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 60G44
Mathematics Subject Classification ID: 62G05
zbMATH DE Number: 5964137
Wikidata QID: Q58692014
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2011/372512
OpenAlex ID: W1977481190
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description: scientific article; zbMATH DE number 5964137