This paper discusses a methodology which uses time series cross sectional datafor the estimation of a time dependent regression function depending on explanatory variables and for the prediction of values of the dependent variable. The methodology assumes independent observations and is based on...

In this note the unobserved component approach underlying the software package SEATS is compared with the Beveridge-Nelson type of decomposition for seasonal time series. The main strength of the SEATS approach lies in the appealing model formulation and the careful specification and adjustment...

We introduce a nonparametric smoothing procedure for nonparametric factor analaysis of multivariate time series. The asymptotic properties of the proposed procedures are derived. We present an application based on the residuals from the Fair macromodel. -- Factor Analysis ; Time Series ; Kernel...

We propose a nonparametric test for checking parametric hypotheses about the stationary density of weakly dependent observations. The test statistic is based on the L2-distance between a nonparametric and a smoothed version of a parametric estimate of the stationary density. It can be shown that...

Stochastic Volatility (SV) models are widely used in financial applications. To decide whether standard parametric restrictions are justified for a given dataset, a statistical test is required. In this paper, we develop such a test based on the linear state space representation. We provide a...

We consider two multivariate long-memory ARCH models, which extend the univariate long-memory ARCH models, we first consider a long-memory extension of the restricted constant conditional correlations (CCC) model introduced by Bollerslev (1990), and we propose a new unrestricted conditional...