Developments in Time Series Analysis

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CRC Press, Jul 1, 1993 - Mathematics - 440 pages
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This volume contains 27 papers, written by time series analysts, dealing with statistical theory, methodology and applications. The emphasis is on the recent developments in the analysis of linear, onlinear (non-Gaussian), stationary and nonstationary time series. The topics include cointegration, estimation and asymptotic theory, Kalman filtering, nonparametric statistical inference, long memory models, nonlinear models, spectral analysis of stationary and nonstationary processes. Quite a number of papers are devoted to modelling and analysis of real time series, and the econometricians, mathematical statisticians, communications engineers and scientists who use time series techniques and Fourier analysis should find the papers in this volume useful.
 

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Contents

Developments in multivariate covariance generation
26
a review
50
The Gaussian log likelihood and stationary sequences
69
Exact maximum likelihood estimation for extended
110
Determining the number of jumps in a spectrum
127
Periodogram analysis for complexvalued time series
149
A spectral approach to long memory time series
164
Nonparametric function estimation in noisy chaos
183
NonGaussian characteristics of exponential autoregressive
257
Bispectrum based checking of linear predictability for time series
274
Maximum likelihood fitting of bilinear models to time series
283
Conditional maximum likelihood estimates for INARl
310
dispersion and modes
331
The prediction of timefrequency spectra using
355
Time variable and state dependent modelling of nonstationary
374
Demodulation of phase modulated signals
414

Nonparametric tests of serial independence
207
Measuring nonlinearity in time series
230

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