S. Sotoca López, M. Jerez Méndez, J. Casals Carro, A. García Hiernaux

Computing the gaussian likelihood for a nonstationary state-space model is a difficult problem which has been tackled within the literature using two strategies: data transformation and diffuse likelihood. The data transformation approach is cumbersome, as it requires nonstandard filtering. On the other hand, in some nontrivial cases the diffuse likelihood value depends on the scale of the diffuse states, so one can obtain different likelihood values corresponding to different observationally equivalent models. In this paper we discuss the properties of the minimally-conditioned likelihood function, as well as two efficient methods to compute its terms with computational advantages for specific models. Three convenient features of the minimally-conditioned likelihood are: (a) it can be computed with standard Kalman filters, (b) it is scale-free, and (c) its values are coherent with those resulting from differencing, this being the most popular approach to deal with nonstationary data.

Palabras clave: state-space models, conditional likelihood, diffuse likelihood, diffuse initial conditions, kalman filter, nonstationarity

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Programado

JC5 Series temporales 1
19 de abril de 2012  12:00
Sala Viena

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