koyckDlm {dLagM} | R Documentation |
Implement distributed lag models with Koyck transformation
Description
Applies distributed lag models with Koyck transformation with one predictor.
Usage
koyckDlm(x , y , intercept)
Arguments
x |
A vector including the observations of predictor time series. This is not restricted to |
y |
A vector including the observations of dependent time series. This is not restricted to |
intercept |
Set to |
Details
To deal with infinite DLMs, we can use the Koyck transformation. When we apply Koyck transformation, we get the following:
When we solve this equation for , we obtain Koyck DLM as follows:
where and the random error after the transformation is
(Judge and Griffiths, 2000).
Then, instrumental variables estimation is employed to fit the model.
The standard function summary()
prints model summary for the model of interest.
AIC/BIC of a fitted KOyck model is displayed by setting the class
attribute of model to lm
. See the example.
Value
model |
An object of class |
geometric.coefficients |
A vector composed of corresponding geometric distributed lag model coefficients. |
Author(s)
Haydar Demirhan
Maintainer: Haydar Demirhan <haydar.demirhan@rmit.edu.au>
References
B.H. Baltagi. Econometrics, Fifth Ed. Springer, 2011.
R.C. Hill, W.E. Griffiths, G.G. Judge. Undergraduate Econometrics. Wiley, 2000.
Examples
data(seaLevelTempSOI)
model.koyck = koyckDlm(x = seaLevelTempSOI$LandOcean,
y = seaLevelTempSOI$GMSL)
summary(model.koyck, diagnostics = TRUE)
residuals(model.koyck)
coef(model.koyck)
fitted(model.koyck)
AIC(model.koyck)
BIC(model.koyck)