Estimates and forecasts UC models
UCestim(sys)
The same input object with the appropriate fields filled in, in particular:
p: Estimated transformed parameters
v: Estimated innovations (white noise in correctly specified models)
yFor: Forecast values of output
yForV: Forecasted values variance
criteria: Value of criteria for estimated model
covp: Covariance matrix of estimated transformed parameters
grad: Gradient of log-likelihood at the optimum
iter: Estimation iterations
an object of type UComp
created with UC
Diego J. Pedregal
UCestim
estimates and forecasts a time series using an
UC model.
The optimization method is a BFGS quasi-Newton algorithm with a
backtracking line search using Armijo conditions.
Parameter names in output table are the following:
Damping: Damping factor for DT trend.
Level: Variance of level disturbance.
Slope: Variance of slope disturbance.
Rho(#): Damping factor of cycle #.
Period(#): Estimated period of cycle #.
Var(#): Variance of cycle #.
Seas(#): Seasonal harmonic with period #.
Irregular: Variance of irregular component.
AR(#): AR parameter of lag #.
MA(#): MA parameter of lag #.
AO#: Additive outlier in observation #.
LS#: Level shift outlier in observation #.
SC#: Slope change outlier in observation #.
Beta(#): Beta parameter of input #.
Cnst: Constant.
Standard methods applicable to UComp objects are print, summary, plot, fitted, residuals, logLik, AIC, BIC, coef, predict, tsdiag.
UC
, UCmodel
, UCvalidate
, UCfilter
,
UCsmooth
, UCdisturb
, UCcomponents
,
UChp
if (FALSE) {
m1 <- UCsetup(log(AirPassengers))
m1 <- UCestim(m1)
}
Run the code above in your browser using DataLab