This paper introduces a class of nonlinear innovation process that has similar properties as the white noise process. Consequently the process can be a replacement of the white noise process in cases where the latter is inadequate as residual process.KEYWORDS: Asymptotic distribution of autocorrelation, nonlinear errors, nonlinear residuals, nonlinear time serie
The aim of the paper is to examine some of the key issues in nonlinear time series analysis. Tools a...
Due to the complexity and nonlinear variety of the real world, nonlinear time series analysis has b...
Use of nonlinear models in analyzing time series data is becoming increasingly popular. This paper c...
This paper presents a nonlinear autoregressive model with Ornstein Uhlenbeck processes innovation ...
AbstractA general procedure for modeling stochastic, nonlinear, dynamic process from time series dat...
Tools and approaches are provided for nonlinear time series modelling in econometrics. A wide range ...
In this paper, we look for new opportunities that can be exploited using some of the recent developm...
In this chapter, we review the problem of testing for nonlinearity in time series. First, we discuss...
summary:Some methods for approximating non-linear AR(1) processes by classical linear AR(1) models a...
We propose an asymptotically distribution-free transform of the sample autocorrelations of residuals...
This paper contains a nonlinear, nonstationary autoregressive model whose intercept changes determin...
SUMMARY: The asymptotic distribution of residual autocorrelations for some very general nonlinear ti...
The object of research. The object of research is modeling and forecasting nonlinear nonstationary p...
In this paper a class of nonparametric transfer function models is proposed to model nonlinear relat...
The paper presents a nonparametric identification method for the determination of the kernels of non...
The aim of the paper is to examine some of the key issues in nonlinear time series analysis. Tools a...
Due to the complexity and nonlinear variety of the real world, nonlinear time series analysis has b...
Use of nonlinear models in analyzing time series data is becoming increasingly popular. This paper c...
This paper presents a nonlinear autoregressive model with Ornstein Uhlenbeck processes innovation ...
AbstractA general procedure for modeling stochastic, nonlinear, dynamic process from time series dat...
Tools and approaches are provided for nonlinear time series modelling in econometrics. A wide range ...
In this paper, we look for new opportunities that can be exploited using some of the recent developm...
In this chapter, we review the problem of testing for nonlinearity in time series. First, we discuss...
summary:Some methods for approximating non-linear AR(1) processes by classical linear AR(1) models a...
We propose an asymptotically distribution-free transform of the sample autocorrelations of residuals...
This paper contains a nonlinear, nonstationary autoregressive model whose intercept changes determin...
SUMMARY: The asymptotic distribution of residual autocorrelations for some very general nonlinear ti...
The object of research. The object of research is modeling and forecasting nonlinear nonstationary p...
In this paper a class of nonparametric transfer function models is proposed to model nonlinear relat...
The paper presents a nonparametric identification method for the determination of the kernels of non...
The aim of the paper is to examine some of the key issues in nonlinear time series analysis. Tools a...
Due to the complexity and nonlinear variety of the real world, nonlinear time series analysis has b...
Use of nonlinear models in analyzing time series data is becoming increasingly popular. This paper c...
DOI
Add to ORKG