tvp.reg {dynmix} | R Documentation |
Computes Time-Varying Parameters Regression.
Description
This function estimates Time-Varying Parameters regression.
Usage
tvp.reg(y,x,lambda=NULL,kappa=NULL,V=NULL,W=NULL)
Arguments
y |
one column |
x |
|
lambda |
optional, see |
kappa |
optional, see |
V |
optional, |
W |
optional, |
Details
If lambda
is specified, then the method described by Raftery et al. (2010) is used, with possible extentsion to the one described by Koop and Korobilis (2012). Otherwise, the Kalman filter described as by Nagy and Suzdaleva (2013) is used.
Value
object of class tvpreg
, i.e., list
of
$y.hat |
|
$coef |
|
$R |
|
$V |
|
References
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.
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.
Examples
data(oil)
t1 <- tvp.reg(y=oil[,1,drop=FALSE],x=oil[,-1,drop=FALSE],lambda=0.99,V=100,W=100)
t2 <- tvp.reg(y=oil[,1,drop=FALSE],x=oil[,-1,drop=FALSE],V=100,W=100)