Add to ORKGAbstract—This paper considers parameter esti-mation for state-space models (SSMs). We pro-pose quasi-likelihood (QL) and asymptotic quasi-likelihood (AQL) approaches for the estimation of state-space models. The asymptotic quasi-likelihood (AQL) utilises a nonparametric kernel estimator of the conditional variance covariances matrix Σt to re-place the true Σt in the standard quasi-likelihood. The kernel estimation avoids the risk of potential miss-specification of Σt and thus make the parameter estimator more robust. This has been further verified by empirical studies carried out in this paper
[[abstract]]I propose a simply method to estimate the regression parameters in quasi-likelihood mode...
We illustrate how to estimate parameters of linear state-space models using the Stata program sspace...
The model parameters of linear state space models are typically estimated with maximum likelihood es...
This paper considers parameter estimation for state-space models (SSMs). We propose quasi-likelihood...
This paper considers parameter estimation for nonlinear and non-Gaussian state-space models with cor...
For estimation of the stochastic volatility model (SVM), this paper suggests the quasi-likelihood (Q...
AbstractFor estimation of the stochastic volatility model (SVM), this paper suggests the quasi-likel...
In this thesis, parameter estimation for multivariate heteroscedastic models with unspecified correl...
State-space models (SSMs) encompass a wide range of popular models encountered in various fields suc...
This paper considers parameter estimation in multivariate heteroscedastic models with unspecific cor...
Asymptotic quasi-likelihood based on kernel smoothing for nonlinear and non-gaussian state-space mod...
AbstractOrdinary quasi-likelihood estimators are based on estimating functions with certain strong o...
Alzghool, R. & Lin, Y. (2011). Initial values in estimation procedures for State Space Models (S...
State space model is a class of models where the observations are driven by underlying stochastic pr...
In this paper, estimation for the generalized autoregressive conditional heteroscedasticity (GARCH) ...
[[abstract]]I propose a simply method to estimate the regression parameters in quasi-likelihood mode...
We illustrate how to estimate parameters of linear state-space models using the Stata program sspace...
The model parameters of linear state space models are typically estimated with maximum likelihood es...
This paper considers parameter estimation for state-space models (SSMs). We propose quasi-likelihood...
This paper considers parameter estimation for nonlinear and non-Gaussian state-space models with cor...
For estimation of the stochastic volatility model (SVM), this paper suggests the quasi-likelihood (Q...
AbstractFor estimation of the stochastic volatility model (SVM), this paper suggests the quasi-likel...
In this thesis, parameter estimation for multivariate heteroscedastic models with unspecified correl...
State-space models (SSMs) encompass a wide range of popular models encountered in various fields suc...
This paper considers parameter estimation in multivariate heteroscedastic models with unspecific cor...
Asymptotic quasi-likelihood based on kernel smoothing for nonlinear and non-gaussian state-space mod...
AbstractOrdinary quasi-likelihood estimators are based on estimating functions with certain strong o...
Alzghool, R. & Lin, Y. (2011). Initial values in estimation procedures for State Space Models (S...
State space model is a class of models where the observations are driven by underlying stochastic pr...
In this paper, estimation for the generalized autoregressive conditional heteroscedasticity (GARCH) ...
[[abstract]]I propose a simply method to estimate the regression parameters in quasi-likelihood mode...
We illustrate how to estimate parameters of linear state-space models using the Stata program sspace...
The model parameters of linear state space models are typically estimated with maximum likelihood es...