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Bootstrapping State-Space Models: Gaussian Maximum Likelihood Estimation and the Kalman Filter
David S. Stoffer and Kent D. Wall
Journal of the American Statistical Association
Vol. 86, No. 416 (Dec., 1991), pp. 1024-1033
Stable URL: http://www.jstor.org/stable/2290521
Page Count: 10
You can always find the topics here!Topics: Standard error, Statistical models, Parametric models, Statistical estimation, Estimators, Kalman filters, Modeling, Histograms, Autoregressive moving average, Economic statistics
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The bootstrap is proposed as a method for assessing the precision of Gaussian maximum likelihood estimates of the parameters of linear state-space models. Our results also apply to autoregressive moving average models, since they are a special case of state-space models. It is shown that for a time-invariant, stable system, the bootstrap applied to the innovations yields asymptotically consistent standard errors. To investigate the performance of the bootstrap for finite sample lengths, simulation results are presented for a two-state model with 50 and 100 observations; two cases are investigated, one with real characteristic roots and one with complex characteristic roots. The bootstrap is then applied to two real data sets, one used in a test for efficient capital markets and one used to develop an autoregressive integrated moving average model for quarterly earnings data. We find the bootstrap to be of definite value over the conventional asymptotics.
Journal of the American Statistical Association © 1991 American Statistical Association