Stationary Time Series
Regression with Stationary Time Series
... with Stationary Time Series ...ran time-series regres- sions based on the Gauss-Markov methodology that we studied ...in time-series analysis using aggregate ...
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A Nonparametric Model for Stationary Time Series
... the stationary time series mixture model described in this ...the stationary and the transition densities, through posterior simulation for the latent model via MCMC, with a Monte Carlo sample ...
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Robust Forecasting of Non-Stationary Time Series
... In this paper we develop a new robust time series forecasting methodology for non-stationary time series. It allows for heteroscedasticity in the data and remains reliable in the ...
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Portmanteau tests for linearity of stationary time series
... of stationary time series based on ‘generalized correlations’ of residuals from a finite-parameter linear model, that is to say autocorrelations and cross-correlations of different powers of the ...
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Portmanteau tests for linearity of stationary time series
... of stationary time series based on generalized correlations of ...to time series of stock returns illustrated the practical use of the ...
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Portmanteau tests for linearity of Stationary Time Series
... Abstract This paper considers the problem of testing for linearity of stationary time series. Portmanteau tests are discussed which are based on generalized correlations of residuals from a linear ...
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Portmanteau tests for linearity of Stationary Time Series
... Abstract This paper considers the problem of testing for linearity of stationary time series. Portmanteau tests are discussed which are based on generalized correlations of residuals from a linear ...
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Spectral Subsampling MCMC for Stationary Time Series
... Our paper extends the applicability of previously proposed subsampling methods to stationary time series. The method is based on using the Fast Fourier Transform (FFT) to eval- uate the likelihood ...
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SLEX analysis of multivariate non-stationary time series
... non-stationary time series using the SLEX (Smooth Localized Complex EXponentials) ...both time and ...non-stationary time series closely parallels traditional Fourier ...
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On the Application of Bootstrap Method to Stationary Time Series Process
... for stationary time series ...pseudo-time series, so that the statistics of interest are calculated along based on the resampled data ...
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Fourier Analysis Of Stationary Time Series In Function Space
... of stationary time series paralleling or extending the results of Horváth, Kokoszka and Reeder (2013), but under different weak dependence conditions; see Corollaries ...dependent stationary ...
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Detecting long-range dependence in non-stationary time series
... In the next section we will develop an estimator of the function d 0 and establish uniform convergency. The integral is then estimated by a Riemann sum and we investigate the asymptotic properties of the resulting ...
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Quasi-likelihood inference for modulated non-stationary time series
... other time series models In this chapter we formally define stochastic processes on the set of integers, and observations from stochastic processes, which are called time ...of time ...
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Clustering stationary and non-stationary time series based on autocorrelation distance of hierarchical and k-means algorithms
... proposed a fuzzy clustering approach based on autocorrelation function, which applied on data which have a strong autocorrelation. In other words, the process of calculating distance value becomes complex and problematic ...
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Construction of stationary time series via the Gibbs sampler with application to volatility models
... strictly stationary time series with a known marginal density and with linear ...correct stationary density to the initial observation, leading to ecient likelihood ...the series to be ...
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Conditional-Sum-of-Squares Estimation ofModels for Stationary Time Series with Long Memory
... Abstract Employing recent results of Robinson (2005) we consider the asymptotic properties of conditional-sum-of-squares (CSS) estimates of parametric models for stationary time series with long ...
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Stationary Time Series in Pricing
... The time series models are mathematical forecasting models that seek to find the dependence of the future value on the past value within the process and calculate the forecast based on this ...
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Non-Stationary Time Series andunitroottests
... the series is stationary, such that normal shocks have transitory effects, the presence of a break will make it look like the shocks have permanent ...
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Reconstructing the Kalman Filter for Stationary and Non Stationary Time Series
... A common way of obtaining well-defined distributions of the level and seasonal effects is to impose the condition that the seed seasonal effects sum to zero. This provides the extra equation required to solve for the ...
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SPECTRAL ANALYSIS OF NON-STATIONARY TIME SERIES
... 1. Introduction; The aim of this paper is to take stock of the important recent contributions to spectral analysis, especially as they apply to non-stationary processes. Non-stationary processes are ...
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