predict.btsr {BTSR}

Predict method for BTSR

Description

Predicted values based on btsr object.

Usage

## S3 method for class 'btsr'
predict(object, newdata, nnew = 0, ...)

Arguments

object

Object of class inheriting from "btsr"

newdata

A matrix with new values for the regressors. If omitted and "xreg" is present in the model, the fitted values are returned. If the model does not include regressors, the functions will use the value of nnew.

nnew

number of out-of-sample forecasts required. If newdata is provided, nnew is ignored.

...

further arguments passed to or from other methods.

Details

predict.btsr produces predicted values, obtained by evaluating the regression function in the frame newdata.

If newdata is omitted the predictions are based on the data used for the fit.

For now, prediction intervals are not provided.

Value

A list with the following arguments

series

The original time series yt.

xreg

The original regressors (if any).

fitted.values

The in-sample forecast given by \mu_t.

etat

In-sample values of g(\mu[t]).

error

The error term (depends on the argument error.scale)

residuals

The (in-sample) residuals, that is, the observed minus the predicted values. Same as error when error.scale = 0

forecast

The predicted values for yt.

TS

only for "BARC" models. The iterated map.

Ts.forecast

only for "BARC" models. The predicted values of the iterated map.

Examples

 #------------------------------------------------------------
 # Generating a Beta model were mut does not vary with time
 # yt ~ Beta(a,b), a = mu*nu, b = (1-mu)*nu
 #------------------------------------------------------------

y <- btsr.sim(model= "BARFIMA", linkg = "linear",
               n = 100, seed = 2021,
               coefs = list(alpha = 0.2, nu = 20))

# fitting the model
f <- btsr.fit(model = "BARFIMA", yt = y, report = TRUE,
             start = list(alpha = 0.5, nu = 10),
             linkg = "linear", d = FALSE)

pred = predict(f, nnew = 5)
pred$forecast