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Stable Innovations (stable + innovation)
Selected AbstractsSUBSAMPLING THE JOHANSEN TEST WITH STABLE INNOVATIONSAUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 1 2010Pu Chen Summary By taking into account the thick-tail property of the errors, cointegration analysis in vector error-correction models with infinite-variance stable errors is a natural generalization of cointegration analysis in error-correction models with normally distributed errors. We study the Johansen test for cointegrated systems under symmetric stable innovations with discrete spectral measures. The results show that the distributions of the Johansen test statistics under these innovations involve nuisance parameters. To overcome the problem of nuisance parameters, we implement a nonparametric subsampling procedure. We document some subsampling simulation results and demonstrate in an empirical example how the test can be used in practice. [source] Simultaneous prediction intervals for ARMA processes with stable innovationsJOURNAL OF FORECASTING, Issue 3 2009John P. Nolan Abstract We describe a method for calculating simultaneous prediction intervals for ARMA times series with heavy-tailed stable innovations. The spectral measure of the vector of prediction errors is shown to be discrete. Direct computation of high-dimensional stable probabilities is not feasible, but we show that Monte Carlo estimates of the interval width is practical. Copyright © 2008 John Wiley & Sons, Ltd. [source] Asymptotic self-similarity and wavelet estimation for long-range dependent fractional autoregressive integrated moving average time series with stable innovationsJOURNAL OF TIME SERIES ANALYSIS, Issue 2 2005Stilian Stoev Primary 60G18; 60E07; Secondary 62M10; 63G20 Abstract., Methods for parameter estimation in the presence of long-range dependence and heavy tails are scarce. Fractional autoregressive integrated moving average (FARIMA) time series for positive values of the fractional differencing exponent d can be used to model long-range dependence in the case of heavy-tailed distributions. In this paper, we focus on the estimation of the Hurst parameter H = d + 1/, for long-range dependent FARIMA time series with symmetric , -stable (1 < , < 2) innovations. We establish the consistency and the asymptotic normality of two types of wavelet estimators of the parameter H. We do so by exploiting the fact that the integrated series is asymptotically self-similar with parameter H. When the parameter , is known, we also obtain consistent and asymptotically normal estimators for the fractional differencing exponent d = H , 1/,. Our results hold for a larger class of causal linear processes with stable symmetric innovations. As the wavelet-based estimation method used here is semi-parametric, it allows for a more robust treatment of long-range dependent data than parametric methods. [source] SUBSAMPLING THE JOHANSEN TEST WITH STABLE INNOVATIONSAUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 1 2010Pu Chen Summary By taking into account the thick-tail property of the errors, cointegration analysis in vector error-correction models with infinite-variance stable errors is a natural generalization of cointegration analysis in error-correction models with normally distributed errors. We study the Johansen test for cointegrated systems under symmetric stable innovations with discrete spectral measures. The results show that the distributions of the Johansen test statistics under these innovations involve nuisance parameters. To overcome the problem of nuisance parameters, we implement a nonparametric subsampling procedure. We document some subsampling simulation results and demonstrate in an empirical example how the test can be used in practice. [source] | ||||||||||