Casals Carro, José and Sotoca López, Sonia and Jerez Méndez, Miguel (2012) Minimally Conditioned Likelihood for a Nonstationary State Space Model. [Working Paper or Technical Report] (Unpublished)
Available under License Creative Commons Attribution Non-commercial.
Official URL: http://eprints.ucm.es/14629/
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.
|Item Type:||Working Paper or Technical Report|
|Uncontrolled Keywords:||State-space models, Conditional likelihood, Diffuse likelihood, Diffuse initial conditions, Kalman filter, Nonstationarity.|
|Subjects:||Social sciences > Economics > Econometrics|
|Series Name:||Documentos de Trabajo del Instituto Complutense de Análisis Económico (ICAE)|
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|Deposited On:||06 Mar 2012 12:24|
|Last Modified:||15 Nov 2013 11:49|
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