fit.KuttnerModel {RGAP}

Maximum likelihood estimation of a KuttnerModel

Description

Estimates a two-dimensional state-space model and performs filtering and smoothing to obtain the output gap.

Usage

## S3 method for class 'KuttnerModel'
fit(
  model,
  parRestr = initializeRestr(model),
  signalToNoise = NULL,
  control = NULL,
  ...
)

Arguments

model

An object of class KuttnerModel.

parRestr

A list of matrices containing the parameter restrictions for the cycle, trend, and the inflation equation. Each matrix contains the lower and upper bound of the involved parameters. NA implies that no restriction is present. Autoregressive parameters are automatically restricted to the stationary region unless box constraints are specified. By default, parRestr is intitialized by the function initializeRestr(model).

signalToNoise

(Optional) signal to noise ratio.

control

(Optional) A list of control arguments to be passed on to optim.

...

additional arguments to be passed to the methods functions.

Value

An object of class KuttnerFit containing the following components:

model

The input object of class KuttnerModel.

SSMfit

The estimation output from the funtcion fitSSM from KFAS.

SSMout

The filtering and smoothing output from the funtcion KFS from KFAS.

parameters

A data frame containing the estimated parameters, including standard errors, t-statistics, and p-values.

fit

A list of model fit criteria (see below).

call

Original call to the function.

The list component fit contains the following model fit criteria:

loglik

Log-likelihood function value.

AIC

Akaike information criterion.

BIC

Bayesian information criterion.

AIC

Hannan-Quinn information criterion.

RMSE

Root mean squared error of the inflation equation.

R2

R squared of the inflation equation.

LjungBox

Ljung-Box test output of the inflation equation.

See Also

Other fitting methods: fit.NAWRUmodel(), fit.TFPmodel(), fit()

Examples

# load data for the Netherlands
data("gap")
country <- "Netherlands"
tsList <- as.list(gap[[country]][, c("cpih", "gdp")])
tsList$infl <- diff(tsList$cpih)
model <- KuttnerModel(tsl = tsList, trend = "RW2", cycleLag = 1, cycle = "AR2", start = 1980)

# estimate Kutter's model
parRestr <- initializeRestr(model = model, type = "hp")

gapKuttner <- fit(model, parRestr, signalToNoise = 1 / 10)