dLagM (version 1.0.2)

koyckDlmForecast: Compute forecasts for Koyck transformation of distributed lag models

Description

A function that computes forecasts for Koyck transformation of distributed lag model with one predictor.

Usage

koyckDlmForecast(model , x , h = 1 , interval = FALSE , alpha = 0.05 , nSim = 500)

Arguments

model

An object of class lm including the fitted model with koyck.dlm() function.

x

A vector including the new observations of independent time series. This is not restricted to ts objects.

h

The forecast horizon.

interval

If TRUE, \((1-\alpha)\%\) confidence intervals for forecasts are displayed along with forecasts.

alpha

Significance level of confidence interval.

nSim

An integer showing the number of Monte Carlo simulations used to compute confidence intervals for forecasts.

Value

forecasts

A vector including forecasts.

Details

This function directly uses the model formula and estimates of model coefficients to find forecast one-by-one starting from the one-step ahead forecast.

Confidence intervals are found by the Monte Carlo approach using a Gaussian error distribution with zero mean and empirical variance of the dependent series.

Examples

# NOT RUN {
data(warming)
model.koyck = koyckDlm(x = warming$NoMotorVehicles , 
y = warming$Warming , show.summary = TRUE)
koyckDlmForecast(model = model.koyck  , x = c(95, 98, 101), h = 3 , 
                interval = FALSE)
koyckDlmForecast(model = model.koyck  , x = c(95, 98, 101), h = 3 , 
                interval = TRUE , nSim = 100)
# }