dLagM (version 0.0.8)

ardlDlm: Implement finite autoregressive distributed lag model

Description

A function that applies autoregressive distributed lag models of order (p , q) with one predictor.

Usage

ardlDlm(x , y , p = 1 , q = 1 , show.summary = TRUE)

Arguments

x

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

y

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

p

An integer representing the order of autoregressive process.

q

An integer representing finite lag length.

show.summary

If TRUE, prints standard model summary for the model of interest.

Value

model

An object of class lm. See the details of lm function.

order

A vector composed of \(p\) and \(q\) orders.

Details

The autoregressive DLM is a flexible and parsimonious infinite DLMs. We write the model ARDL\((p, q)\) as

$$ Y_{t} = \mu+ \beta_{0}X_{t}+\beta_{1}X_{t-1}+\cdots +\beta_{p}X_{t-p}+\gamma_{1}Y_{t-1}+\cdots+\gamma_{q}Y_{t-q}+e_{t}. $$

References

B.H. Baltagi. Econometrics, Fifth Ed. Springer, 2011.

R.C. Hill, W.E. Griffiths, G.G. Judge. Undergraduate Econometrics. Wiley, 2000.

Examples

# NOT RUN {
data(warming)
model.ardl = ardlDlm(x = warming$NoMotorVehicles, 
y = warming$Warming, p = 1 , q = 1 , show.summary = TRUE)
# }