Add to ORKGWe construct efficient estimators of the identifiable parameters in a regression model when the errors follow a stationary parametric ARCH(P) process. We do not assume a functional form for the conditional density of the errors, but do require that it be symmetric about zero. The estimators of the mean parameters are adaptive in the sense of Bickel [2]. The ARCH parameters are not jointly identifiable with the error density. We consider a reparameterization of the variance process and show that the identifiable parameters of this process are adaptively estimable
The autoregressive conditional heteroscedastic (ARCH) model and its extensions have been widely used...
We focus on the linear model with conditional heteroskedasticity of unknown form. "Adaptive" estimat...
A standard assumption while deriving the asymptotic distribution of the quasi maximum likelihood est...
We construct efficient estimators of the identifiable parameters in a regression model when the errors...
We construct efficient estimators of the identifiable parameters in a regression model when the erro...
Abstract. We consider a model Yt = σtηt in which (σt) is not independent of the noise process (ηt), ...
Existing specification tests for conditional heteroskedasticity are derived under the assumption that...
We consider parameter estimation for a class of ARCH(∞) models, which do not necessarily have a para...
Abstract: In this paper, we have two asymptotic objectives: the LAN and the residual empirical proce...
A companion paper (Nelson (1992)) showed that in data observed at high frequencies, an ARCH model ma...
In this paper the class of ARCH(∞) models is generalized to the nonsta-tionary class of ARCH(∞) mode...
It is well-known that financial data sets exhibit conditional heteroskedasticity.GARCH type models a...
Consider some ARCH() process, say ARCH(), where with a Gaussian (strong) white noise . n=500 a1=...
In this paper, we develop a complete methodology for semiparametric inference in the time-varying AR...
We consider parameter estimation for a class of ARCH(∞) models, which do not necessarily have a para...
The autoregressive conditional heteroscedastic (ARCH) model and its extensions have been widely used...
We focus on the linear model with conditional heteroskedasticity of unknown form. "Adaptive" estimat...
A standard assumption while deriving the asymptotic distribution of the quasi maximum likelihood est...
We construct efficient estimators of the identifiable parameters in a regression model when the errors...
We construct efficient estimators of the identifiable parameters in a regression model when the erro...
Abstract. We consider a model Yt = σtηt in which (σt) is not independent of the noise process (ηt), ...
Existing specification tests for conditional heteroskedasticity are derived under the assumption that...
We consider parameter estimation for a class of ARCH(∞) models, which do not necessarily have a para...
Abstract: In this paper, we have two asymptotic objectives: the LAN and the residual empirical proce...
A companion paper (Nelson (1992)) showed that in data observed at high frequencies, an ARCH model ma...
In this paper the class of ARCH(∞) models is generalized to the nonsta-tionary class of ARCH(∞) mode...
It is well-known that financial data sets exhibit conditional heteroskedasticity.GARCH type models a...
Consider some ARCH() process, say ARCH(), where with a Gaussian (strong) white noise . n=500 a1=...
In this paper, we develop a complete methodology for semiparametric inference in the time-varying AR...
We consider parameter estimation for a class of ARCH(∞) models, which do not necessarily have a para...
The autoregressive conditional heteroscedastic (ARCH) model and its extensions have been widely used...
We focus on the linear model with conditional heteroskedasticity of unknown form. "Adaptive" estimat...
A standard assumption while deriving the asymptotic distribution of the quasi maximum likelihood est...