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FUNDAMENTAL CONCEPTS IN TIME
MODELS FOR STATIONARY TIME SERIES
MODELS FOR NONSTATIONARY TIME
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ARIMA models ARIMA predictions ARIMA process ARMA autocorrelation function autocovariances autoregressive processes behavior Chap coefficients composite predictions computation conditional expectation forecast confidence intervals consider correlation covariance denoted differences differencing durables E(zt economic equations evaluation EWMA example expenditures on producers exponential smoothing expression FMP model FMP predictions forecast errors forecast profile given horizon hypothesis income invertible joint distribution lags linear log-normal log-normal distribution matrix mean square error moving-average process nonstationary number of observations o o o o one-step-ahead forecast output partial autocorrelations particular past observations plotted in Fig postsample procedure random variable random walk random-shock form raw data realization residuals sample autocorrelations sample correlogram sample period seasonal model seasonally adjusted series analysis simply standard errors stationarity stationary stationary process statistical stochastic process structure tions tool sales series UJ UJ UJ unemployment rate variance weights zero zt-i,zt zT+i