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.
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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.
- 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.
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