Exact Score for Time Series Models in State Space Form (Now published in Biometrika (1992), 79, 4, pp.283-6.)

Siem Jan Koopman and Neil Shephard ()

STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE

Abstract: The score vector for a time series model which fits into the Gaussian state space form can be approximated by numerically differentiating the log-likelihood. If the parameter vector is of length p, this involves the running of p + 1 Kalman filters. This paper shows the score vector can be computed in a single pass of the Kalman filter and a smoother. For many classes of models this dramatically increases the speed and reliability of algorithms for the numerical maximisation of likelihood.

Keywords: Smoothing; Kahman filter; EM algorithm; unobserved components model; profile likelihood. (search for similar items in EconPapers)
Date: 1992
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:cep:stiecm:241

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