This IMA Volume in Mathematics and its Applications NEW DIRECTIONS IN TIME SERIES ANALYSIS, PART II is based on the proceedings of the IMA summer program "New Directions in Time Series Analysis. " We are grateful to David Brillinger, Peter Caines, John Geweke, Emanuel Parzen, Murray Rosenblatt, and Murad Taqqu for organizing the program and we hope that the remarkable excitement and enthusiasm of the participants in this interdisciplinary effort are communicated to the reader. A vner Friedman Willard Miller, Jr. PREFACE Time Series Analysis is truly an interdisciplinary field because development of its theory and methods requires interaction between the diverse disciplines in which it is applied. To harness its great potential, strong interaction must be encouraged among the diverse community of statisticians and other scientists whose research involves the analysis of time series data. This was the goal of the IMA Workshop on "New Directions in Time Series Analysis. " The workshop was held July 2-July 27, 1990 and was organized by a committee consisting of Emanuel Parzen (chair), David Brillinger, Murray Rosenblatt, Murad S. Taqqu, John Geweke, and Peter Caines. Constant guidance and encouragement was provided by Avner Friedman, Director of the IMA, and his very helpful and efficient staff. The workshops were organized by weeks. It may be of interest to record the themes that were announced in the IMA newsletter describing the workshop: l.

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Part of a two volume set based on a recent IMA program of the same name. The goal of the program and these books is to develop a community of statistical and other scientists kept up-to-date on developments in this quickly evolving and interdisciplinary field. Consequently, these books present recent material by distinguished researchers. Topics discussed in Part I include nonlinear and non- Gaussian models and processes (higher order moments and spectra, nonlinear systems, applications in astronomy, geophysics, engineering, and simulation) and the interaction of time series analysis and statistics (information model identification, categorical valued time series, nonparametric and semiparametric methods). Self-similar processes and long-range dependence (time series with long memory, fractals, 1/f noise, stable noise) and time series research common to engineers and economists (modeling of multivariate and possibly non-stationary time series, state space and adaptive methods) are discussed in Part II.

Inhalt
Recent developments in location estimation and regression for long-memory processes.- Phase-transition in statistical physical models with discrete and continuous symmetries.- Identification of linear systems from noisy data.- Unit roots in U.S. macroeconomic time series: A survey of classical and Bayesian perspectives.- A nonparametric approach to nonlinear time series analysis: Estimation and simulation.- Asymptotics of predictive stochastic complexity.- Smoothness priors.- An extension of quadrature-based methods for solving Euler conditions.- Long memory shot noises and limit theorems with application to Burgers' equation.- On approximate modeling of linear Gaussian processes.- On the identification and prediction of nonlinear models.- Identification of stochastic time-varying parameters.- Convergence of Aström-Wittenmark's self-tuning regulator and related topics.- On the closure of several sets of ARMA and linear state space models with a given structure.- Weak convergence to self-affine processes in dynamical systems.- Recursive estimation in ARMAX models.- On adaptive stabilization and ergodic behaviour of systems with Jump-Markov parameters via nonlinear filtering.- The convergence of output error recursions in infinite order moving average noise.- Linear models with long-range dependence and with finite or infinite variance.- Posterior analysis of possibly integrated time series with an application to real GNP.- On network structure function computations.- Asymptotic properties of estimates in incorrect ARMA models for long-memory time series.