Minimally Conditioned Likelihood for a Nonstationary State Space Model
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...
| Autores: | , , |
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 2012 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/49063 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/49063 |
| Access Level: | acceso abierto |
| Palabra clave: | State-space models Conditional likelihood Diffuse likelihood Diffuse initial conditions Kalman filter Nonstationarity. Econometría (Economía) 5302 Econometría |
| Sumario: | 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 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, being this the most popular approach to deal with nonstationary data. |
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