Inference for random coefficient volatility models

A. Thavaneswaran, You Liang and Julieta Frank

Statistics & Probability Letters, 2012, vol. 82, issue 12, 2086-2090

Abstract: Estimating functions have been shown to be convenient to study inference for nonlinear time series models. One such model is the recently proposed Random Coefficient Autoregressive (RCA) model with Generalized Autoregressive Heteroscedasticity (GARCH) errors (Thavaneswaran et al., 2009). We derive the martingale estimating functions for the joint estimation of the conditional mean and variance parameters and we show the information gain relative to conditional least square estimation.

Keywords: Estimating functions; Nonlinear time series; Information; RCA models; GARCH models (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167715212002805
Full text for ScienceDirect subscribers only

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:eee:stapro:v:82:y:2012:i:12:p:2086-2090

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.spl.2012.07.008

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().