mixest1 | R Documentation |
This function estimates recursively mixtures with state-space components with a dynamic model of switching. The components are normal linear models. Suppose there are available k
potentially important predictors of y
, i.e., x_1, \dots, x_k
. Then up to 2^{k}
linear models including constant term can be created by inclding or not including each of these predictors in the individual model, i.e., component of the mixture.
mixest1(y,x,mods=NULL,ftype=NULL,lambda=NULL,kappa=NULL,V=NULL,W=NULL,atype=NULL)
y |
one column |
x |
|
mods |
optional, |
ftype |
optional, |
lambda |
optional, |
kappa |
optional, |
V |
optional, |
W |
optional, |
atype |
optional, |
object of class mixest
, i.e., list
of
$y.hat |
|
$rvi |
|
$coef |
|
$weights |
|
$V |
|
$R |
|
$components |
|
$parameters |
|
$data.last |
|
Nagy, I., Suzdaleva, E., 2013, Mixture estimation with state-space components and Markov model of switching. Applied Mathematical Modelling 37, 9970–9984.
Barbieri, M. M., Berger, J. O., 2004, Optimal predictive model selection. The Annals of Statistics 32, 870–897.
Burnham, K. P., Anderson, D. R., 2002, Model Selection and Multimodel Inference, Springer.
Karny, M. (ed.), 2006, Optimized Bayesian Dynamic Advising, Springer.
Koop, G., Korobilis, D., 2012, Forecasting inflation using Dynamic Model Averaging. International Economic Review 53, 867–886.
Nagy, I., Suzdaleva, E., 2017, Algorithms and Programs of Dynamic Mixture Estimation, Springer.
Quarteroni, A., Sacco, R., Saleri, F., 2007, Numerical Mathematics, Springer.
Raftery, A. E., Karny, M., Ettler, P., 2010, Online prediction under model uncertainty via Dynamic Model Averaging: Application to a cold rolling mill. Technometrics 52, 52–66.
mixest2
data(oil)
m1 <- mixest1(y=oil[,1,drop=FALSE],x=oil[,-1,drop=FALSE],ftype=1,V=100,W=100)
# Models with only one variable
mods <- diag(1,nrow=ncol(oil[,-1,drop=FALSE]),ncol=ncol(oil[,-1,drop=FALSE]))
mods <- cbind(1,mods)
m2 <- mixest1(y=oil[,1,drop=FALSE],x=oil[,-1,drop=FALSE],mods=mods,ftype=1,V=100,W=100)
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