Time Series Analysis: Forecasting and ControlThis is a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970. It focuses on practical techniques throughout, rather than a rigorous mathematical treatment of the subject. It explores the building of stochastic (statistical) models for time series and their use in important areas of application forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control. Features sections on: recently developed methods for model specification,such as canonical correlation analysis and the use of model selection criteria; results on testing for unit root nonstationarity in ARIMA processes; the state space representation of ARMA models and its use for likelihood estimation and forecasting; score test for model checking; and deterministic components and structural components in time series models and their estimation based on regression-time series model methods. |
Other editions - View all
Common terms and phrases
a₁ approximate ARIMA model ARIMA process ARMA ARMA(p autocovariance function autoregressive operator autoregressive process behavior calculated canonical correlation Chapter coefficients complementary function computed conditional expectations consider covariance matrix damped exponentials damped sine waves deviation difference equation differencing distribution estimated autocorrelations eventual forecast function example finite first-order fitted forecast errors given Hence identification infinite invertible Kalman filter lead least squares likelihood function linear maximum likelihood mean square error minimum mean square moving average process nonstationary observations obtained origin p₁ parameters partial autocorrelation function periodogram polynomial process of order quadratic random shock recursively roots sample Section sine waves solution spectrum square error forecast standard error starting values stationary process stochastic sum of squares Table tion unit circle updating variance vector Vz₁ w₁ weights white noise Y₁ z₁ zero θα λι λο Ρι σ² σα Φι