Semiparametric Estimation in the Multivariate Liouville Model

Semiparametric Multivariate GARCH Model∗

To capture the missed information in the standardized errors by parametric multivariate generalized autoregressive conditional heteroskedasticity (MV-GARCH) model, we propose a new semiparametric MV-GARCH (SM-GARCH) model. This SM-GARCH model is a twostep model: firstly estimating parametric MV-GARCH model, then using nonparametric skills to model the conditional covariance matrix of the standa...

Semiparametric Approximation Methods in Multivariate Model Selection

In this paper we propose a cross-validation selection criterion to determine asymptotically the correct model among the family of all possible partially linear models when the underlying model is a partially linear model. We establish the asymptotic consistency of the criterion. In addition, the criterion is illustrated using two real sets of data. © 2001 Elsevier Science

Adaptive estimation of the functional component in a semiparametric multivariate partially linear model

Efficient estimation of parameters in marginals in semiparametric multivariate models∗

We consider a general multivariate model where univariate marginal distributions are known up to a common parameter vector and we are interested in estimating that vector without assuming anything about the joint distribution, except for the marginals. If we assume independence between the marginals and maximize the resulting quasilikelihood, we obtain a consistent but inefficient estimate. If ...

Semiparametric Estimation in Multivariate Nonstationary Time Series Models

A system of multivariate semiparametric nonlinear time series models is studied with possible dependence structures and nonstationarities in the parametric and nonparametric components. The parametric regressors may be endogenous while the nonparametric regressors are assumed to be strictly exogenous. The parametric regressors may be stationary or nonstationary and the nonparametric regressors ...