Titlebook: New Directions in Time Series Analysis; Part II David Brillinger,Peter Caines,Murad S. Taqqu Conference proceedings 1993 Springer-Verlag Ne

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Identification of Linear Systems from Noisy Data,ion here is that the systems are not uniquely determined from the (ensemble) second moments of the observations. In this paper we analyze certain properties of the set of all observationally equivalent systems. In addition we derive continuity results for the relation between the spectral densities

Bother

Unit Roots in U.S. Macroeconomic Time Series: A Survey of Classical and Bayesian Perspectives, theoretical and statistical implications which have led to the development and widespread application of “unit root” tests. This paper provides a summary of the implications of unit roots in these data, and surveys the procedures and results of Classical and Bayesian investigations of this issue, e

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A Nonparametric Approach to Nonlinear Time Series Analysis: Estimation and Simulation,sity depends on a finite number of lags. Such a density can be represented as a Hermite expansion. Certain parameters of the expansion can be set to imply sharp restrictions on the process such as a pure VAR, a pure ARCH, a nonlinear process with homogeneous innovations, etc. The model is fitted usi

出血

Asymptotics of Predictive Stochastic Complexity,vey of the early developments and the ideas which culminated in a basic inequality on universal codes. The rest of the paper is devoted to results showing that the lower bound given by the basic inequality is achieved in many important cases. The asymptotic behaviour of three kinds of predictive sto

繼承人

Smoothness Priors,igin of the subject is a smoothing problem posed by Whittaker (1923). Smoothness priors is treated here from a stochastic regression-linear model-Gaussian disturbances framework. This conceptually simple framework has broad applications in time series analysis. A reproducing kernel Hilbert space, (R

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On the Identification and Prediction of Nonlinear Models,ecurrence’s function on the higher order moments. We establish the recurrent relations on the third order moment for a general superdiagonal model .(., ., ., .), and for this model we obtain the asymptotic distribution of Glasbey’s statistic which permits to have a consistency method to identify the