Forecasting, Structural Time Series Models and the Kalman Filter¶
Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Object Permanence
- Database theory and accounting — entities (customers, assets, accounts) persist across periods of no transactional activity, with the store maintaining state through silent intervals and re-binding to the same entity on the next transaction; inventory audits reconcile a period's closing stock with the next period's opening stock. Latent-variable econometrics — unobservable quantities (potential GDP, NAIRU, underlying inflation) are modeled as persisting between data releases, with state-space models and Kalman filters formalizing permanence for latent state.
This sourceDevelops state-space models and the Kalman filter for econometrics, formalizing persistence of unobservable latent variables (e.g., trends, underlying components) between data releases.
Supported in partVerified against the publisher's abstract
The abstract confirms unobserved components modeled with state-space models and the Kalman filter in econometrics, but names no specific series and no persistence 'between data releases'.
“structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. ... From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter.”
- Database theory and accounting — entities (customers, assets, accounts) persist across periods of no transactional activity, with the store maintaining state through silent intervals and re-binding to the same entity on the next transaction; inventory audits reconcile a period's closing stock with the next period's opening stock. Latent-variable econometrics — unobservable quantities (potential GDP, NAIRU, underlying inflation) are modeled as persisting between data releases, with state-space models and Kalman filters formalizing permanence for latent state.
Verification¶
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