predict: Forecast for Autoregressive censored models with Normal and...

In ARCensReg: Fitting Univariate Censored Linear Regression Model with Autoregressive Errors

Forecast for Autoregressive censored models with Normal and Student-t innovations

Description

Forecast from models fitted by ARCensReg and ARtCensReg.

Usage

  ## S3 method for class 'ARpCRM'
predict(object, x_pred, ...)
  
  ## S3 method for class 'ARtpCRM'
predict(object, x_pred, ...)

Arguments

object	An object inheriting from class ARpCRM or ARtpCRM, representing a fitted AR(p) censored linear model.
x_pred	Matrix of covariates for responses to be predicted.
...	Further arguments passed to or from other methods.

Value

A time series of predictions.

Author(s)

Fernanda L. Schumacher, Katherine L. Valeriano, Victor H. Lachos, Christian E. Galarza, and Larissa A. Matos

References

\insertRef

schumacher2017censoredARCensReg

\insertRef

valeriano2021censoredARCensReg

See Also

ARCensReg, ARtCensReg

Examples

# Generating a sample
set.seed(2839)
n = 210
x = cbind(1, rnorm(n))
dat = rARCens(n=n, beta=c(-1,2), phi=.5, sig2=.3, x=x, cens='left', pcens=.1)

# Fitting the model
data1 = dat$data[1:205,]
fit = ARCensReg(data1$cc, data1$lcl, data1$ucl, data1$y, x[1:205,],
                 M=5, pc=.12, tol=0.001)

# Forecast
y_pred = predict(fit, x[206:n,])
mean((dat$data$y[206:n] - y_pred)^2) # MSPE

ARCensReg documentation built on Aug. 30, 2023, 1:09 a.m.