dLagM (version 1.0.2)

dLagM-package: Implementation of Time Series Regression Models with Distributed Lag Models

Description

Provides time series regression models with one predictor using finite distributed lag models, polynomial (Almon) distributed lag models, geometric distributed lag models with Koyck transformation, and autoregressive distributed lag models. It also consists of functions for computation of h-step ahead forecasts from these models. See Baltagi (2011) for more information.

Details

Package:	dLagM
Type:	Package
Version:	1.0.2
Date:	2017-01-13
License:	GPL-3

To implement time series regression with finite distributed lag models use dlm function.

To implement time series regression with polynomial distributed lag models use polyDlm function.

To implement time series regression with geometric distributed lag models with Koyck transformation use koyckDlm function.

To implement time series regression with autoregressive distributed lag models use ardlDlm function.

References

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

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

See Also

dlm, polyDlm, koyckDlm, ardlDlm

Examples

# NOT RUN {
# --- For examples, please refer to specific functions ---
# }