RT Report
T1 Minimally Conditioned Likelihood for a Nonstationary State Space Model
A1 Casals Carro, José
A1 Sotoca López, Sonia
A1 Jerez Méndez, Miguel
AB Computing the gaussian likelihood for a nonstationary state-space model is a difficult problem which has been tackled by the literature using two main strategies: data transformation and diffuse likelihood. The data  transformation approach is cumbersome, as it requires nonstandard filtering. On the other hand, in some nontrivialcases the diffuse likelihood value depends on the scale of the diffuse states, so one can obtain different likelihood values corresponding to different observationally equivalentmodels. In this paper we discuss the properties of the minimally-conditioned likelihood function, as well as two efficient methods to compute its terms with computationaladvantages 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, being this the most popular approach to deal with nonstationary data.
YR 2012
FD 2012-03
LK https://hdl.handle.net/20.500.14352/49063
UL https://hdl.handle.net/20.500.14352/49063
LA eng
DS Docta Complutense
RD 10 abr 2025