In this paper, we describe and compare three methods that can be used in forecasting chaotic time series. We simulate four well known chaos and apply the methods developed here. We discuss the existence of measure noise and structural noise and their impact on predictions. Particular attention...

This paper employs response surface regressions based on simulation experiments to calculate asymptotic distribution functions of the tests for cointegration proposed by Johansen. The paper provides accurate tables of asymptotic critical values. A program which can be used to calculate both...

In this paper we address the issue of the efficient estimation of the cointegrating vector in linear regression models with variables that follow general (higher order and fractionally) integrated processes.

In this paper we address the issue of the efficient estimation of the cointegrating vector in linear regression models with variables that follow general (higher order and fractionally) integrated processes.

We show how to apply the fully-modified estimation method in the integrated seemingly unrelation regressions model. Three different fully-modified estimators are studied and their asymptotic distributions are found.

It is shown that the maximum likelihood estimator of a local to unity parameter can be consistently estimated with panel data when the cross section observations are independent. Consistency applies when there are no deterministic trends or when there is a homogenous deterministic trend in the...

In the analysis of time series, it is frequent to classify perturbations as Additive Outliers (AO), Innovative Outliers (IO), Level Shift (LS) outliers or Transitory Change (TC) outliers. In this paper, a new outlier type, the Seasonal Level Shift (SLS), is introduced in order to complete the...

In the analysis of time series, it is frequent to classify perturbations as Additive Outliers (AO), Innovative Outliers (IO), Level Shift (LS) outliers or Transitory Change (TC) outliers. In this paper, a new outlier type, the Seasonal Level Shift (SLS), is introduced in order to complete the...

The paper deals with estimation of missing observations in possibly nonstationary ARIMA models. First, the model is assumed known, and the structure of the interpolation filter is analysed. Using the inverse or dual autocorrelation function it is seen how estimation of a missing observation is...

The segmentation problem arises in many applications in data mining, A.I. and statistics. In this paper, we consider segmenting simple time series. We develop two Bayesian approaches for segmenting a time series, namely the Bayes Factor approach, and the Minimum Message Length (MML) approach. We...